diff --git a/python/tempo/resample.py b/python/tempo/resample.py
index 919d6f01..dca1c0dc 100644
--- a/python/tempo/resample.py
+++ b/python/tempo/resample.py
@@ -4,7 +4,8 @@
 from pyspark.sql.window import Window
 
 # define global frequency options
-
+MUSEC = 'microsec'
+MS = 'ms'
 SEC = 'sec'
 MIN = 'min'
 HR = 'hr'
@@ -17,9 +18,9 @@
 average = "mean"
 ceiling = "ceil"
 
-freq_dict = {'sec' : 'seconds', 'min' : 'minutes', 'hr' : 'hours', 'day' : 'days', 'hour' : 'hours'}
+freq_dict = {'microsec' : 'microseconds','ms' : 'milliseconds','sec' : 'seconds', 'min' : 'minutes', 'hr' : 'hours', 'day' : 'days', 'hour' : 'hours'}
 
-allowableFreqs = [SEC, MIN, HR, DAY]
+allowableFreqs = [MUSEC, MS, SEC, MIN, HR, DAY]
 allowableFuncs = [floor, min, max, average, ceiling]
 
 def __appendAggKey(tsdf, freq = None):
@@ -41,7 +42,7 @@ def aggregate(tsdf, freq, func, metricCols = None, prefix = None, fill = None):
     :param tsdf: input TSDF object
     :param func: aggregate function
     :param metricCols: columns used for aggregates
-    :param prefix the metric columns with the aggregate named function
+    :param prefix: the metric columns with the aggregate named function
     :param fill: upsample based on the time increment for 0s in numeric columns
     :return: TSDF object with newly aggregated timestamp as ts_col with aggregated values
     """
@@ -118,13 +119,22 @@ def aggregate(tsdf, freq, func, metricCols = None, prefix = None, fill = None):
 
 
 def checkAllowableFreq(freq):
+    """
+    Parses frequency and checks against allowable frequencies
+    :param freq: frequncy at which to upsample/downsample, declared in resample function
+    :return: list of parsed frequency value and time suffix
+    """
     if freq not in allowableFreqs:
       try:
           periods = freq.lower().split(" ")[0].strip()
           units = freq.lower().split(" ")[1].strip()
       except:
-          raise ValueError("Allowable grouping frequencies are sec (second), min (minute), hr (hour), day. Reformat your frequency as <integer> <day/hour/minute/second>")
-      if units.startswith(SEC):
+          raise ValueError("Allowable grouping frequencies are microsecond (musec), millisecond (ms), sec (second), min (minute), hr (hour), day. Reformat your frequency as <integer> <day/hour/minute/second>")
+      if units.startswith(MUSEC):
+          return (periods, MUSEC)
+      elif units.startswith(MS) | units.startswith("millis"):
+          return (periods, MS)
+      elif units.startswith(SEC):
           return (periods, SEC)
       elif units.startswith(MIN):
           return (periods, MIN)
diff --git a/python/tests/tsdf_tests.py b/python/tests/tsdf_tests.py
index 8ae2ce86..b75c9a6e 100644
--- a/python/tests/tsdf_tests.py
+++ b/python/tests/tsdf_tests.py
@@ -714,6 +714,51 @@ def test_resample(self):
         # test bars summary
         self.assertDataFramesEqual(bars, barsExpected)
 
+    def test_resample_millis(self):
+        """Test of resampling for millisecond windows"""
+        schema = StructType([StructField("symbol", StringType()),
+                             StructField("date", StringType()),
+                             StructField("event_ts", StringType()),
+                             StructField("trade_pr", FloatType()),
+                             StructField("trade_pr_2", FloatType())])
+
+        expectedSchema = StructType([StructField("symbol", StringType()),
+                                     StructField("event_ts", StringType()),
+                                     StructField("floor_trade_pr", FloatType()),
+                                     StructField("floor_date", StringType()),
+                                     StructField("floor_trade_pr_2", FloatType())])
+
+        expectedSchemaMS = StructType([StructField("symbol", StringType()),
+                                          StructField("event_ts", StringType(), True),
+                                          StructField("date", DoubleType()),
+                                          StructField("trade_pr", DoubleType()),
+                                          StructField("trade_pr_2", DoubleType())])
+
+
+        data = [["S1", "SAME_DT", "2020-08-01 00:00:10.12345", 349.21, 10.0],
+                ["S1", "SAME_DT", "2020-08-01 00:00:10.123", 340.21, 9.0],
+                ["S1", "SAME_DT", "2020-08-01 00:00:10.124", 353.32, 8.0]]
+
+        expected_data_ms = [
+            ["S1", "2020-08-01 00:00:10.123", None, 344.71, 9.5],
+            ["S1", "2020-08-01 00:00:10.124", None, 353.32, 8.0]
+        ]
+
+        # construct dataframes
+        df = self.buildTestDF(schema, data)
+        dfExpected = self.buildTestDF(expectedSchemaMS, expected_data_ms)
+
+        # convert to TSDF
+        tsdf_left = TSDF(df, partition_cols=["symbol"])
+
+        # 30 minute aggregation
+        resample_ms = tsdf_left.resample(freq="ms", func="mean").df.withColumn("trade_pr", F.round(F.col('trade_pr'), 2))
+
+        int_df = TSDF(tsdf_left.df.withColumn("event_ts", F.col("event_ts").cast("timestamp")), partition_cols = ['symbol'])
+        interpolated = int_df.interpolate(freq='ms', func='floor', method='ffill')
+        self.assertDataFramesEqual(resample_ms, dfExpected)
+
+
     def test_upsample(self):
         """Test of range stats for 20 minute rolling window"""
         schema = StructType([StructField("symbol", StringType()),