diff --git a/doc/under_sampling.rst b/doc/under_sampling.rst
index 9f2795430..8c2f91f70 100644
--- a/doc/under_sampling.rst
+++ b/doc/under_sampling.rst
@@ -347,10 +347,29 @@ place. The class can be used as::
 Our implementation offer to set the number of seeds to put in the set :math:`C`
 originally by setting the parameter ``n_seeds_S``.
 
-:class:`NeighbourhoodCleaningRule` will focus on cleaning the data than
-condensing them :cite:`laurikkala2001improving`. Therefore, it will used the
-union of samples to be rejected between the :class:`EditedNearestNeighbours`
-and the output a 3 nearest neighbors classifier. The class can be used as::
+Neighbourhood Cleaning Rule
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The :class:`NeighbourhoodCleaningRule` is another "cleaning" algorithm. It removes
+samples from the majority class that are the closest to the boundary they form with
+the samples of the minority class :cite:`laurikkala2001improving`.
+
+The :class:`NeighbourhoodCleaningRule` expands on the cleaning performed by
+:class:`EditedNearestNeighbours` by eliminating additional majority class samples.
+
+The procedure for the :class:`NeighbourhoodCleaningRule` is as follows:
+
+1. Remove observations from the majority class with edited nearest neighbors (ENN).
+2. Remove additional samples from the majority class if they are one of the k closest
+neighbors of a minority sample, where all or most of those neighbors are not minority.
+
+To carry out step 2 there is one condition: a sample will only be removed if its class
+has a minimum number of observations. The minimum number of observations is regulated
+by the `threshold_cleaning` parameter. A sample will only be removed from the target
+class if it has at least as many observations as threshold times the number of samples
+in the minority class.
+
+The class can be used as::
 
   >>> from imblearn.under_sampling import NeighbourhoodCleaningRule
   >>> ncr = NeighbourhoodCleaningRule(n_neighbors=11)
diff --git a/imblearn/under_sampling/_prototype_selection/_neighbourhood_cleaning_rule.py b/imblearn/under_sampling/_prototype_selection/_neighbourhood_cleaning_rule.py
index 188ba32f3..ee13b4c17 100644
--- a/imblearn/under_sampling/_prototype_selection/_neighbourhood_cleaning_rule.py
+++ b/imblearn/under_sampling/_prototype_selection/_neighbourhood_cleaning_rule.py
@@ -29,7 +29,7 @@
 class NeighbourhoodCleaningRule(BaseCleaningSampler):
     """Undersample based on the neighbourhood cleaning rule.
 
-    This class uses ENN and a k-NN to remove noisy samples from the datasets.
+    This class uses ENN and a k-NN to remove noisy samples from the majority classes(s).
 
     Read more in the :ref:`User Guide <condensed_nearest_neighbors>`.
 
@@ -46,7 +46,8 @@ class NeighbourhoodCleaningRule(BaseCleaningSampler):
         If ``int``, size of the neighbourhood to consider to compute the
         K-nearest neighbors. If object, an estimator that inherits from
         :class:`~sklearn.neighbors.base.KNeighborsMixin` that will be used to
-        find the nearest-neighbors. By default, it will be a 3-NN.
+        find the nearest-neighbors. By default, it explores the 3 closest
+        neighbors.
 
     kind_sel : {{"all", "mode"}}, default='all'
         Strategy to use in order to exclude samples in the ENN sampling.
@@ -65,13 +66,14 @@ class NeighbourhoodCleaningRule(BaseCleaningSampler):
            `"all"` strategy.
 
     threshold_cleaning : float, default=0.5
-        Threshold used to whether consider a class or not during the cleaning
-        after applying ENN. A class will be considered during cleaning when:
+        Threshold used to determine if further samples will be removed from a certain
+        majority class during the cleaning step that follows the ENN. Additional
+        samples will be removed during the second step when:
 
         Ci > C x T ,
 
-        where Ci and C is the number of samples in the class and the data set,
-        respectively and theta is the threshold.
+        where Ci is the number of samples in the class to be under-sampled, C 
+        is the number of samples in the data set, and T is the threshold.
 
     {n_jobs}
 
@@ -79,18 +81,18 @@ class NeighbourhoodCleaningRule(BaseCleaningSampler):
     ----------
     sampling_strategy_ : dict
         Dictionary containing the information to sample the dataset. The keys
-        corresponds to the class labels from which to sample and the values
+        correspond to the class labels from which to sample and the values
         are the number of samples to sample.
 
     edited_nearest_neighbours_ : estimator object
         The edited nearest neighbour object used to make the first resampling.
 
     nn_ : estimator object
-        Validated K-nearest Neighbours object created from `n_neighbors` parameter.
+        Validated K-nearest Neighbours object created from the `n_neighbors` parameter.
 
     classes_to_clean_ : list
-        The classes considered with under-sampling by `nn_` in the second cleaning
-        phase.
+        The classes that statisfy the condition for further under-sampling during the
+        second cleaning phase.
 
     sample_indices_ : ndarray of shape (n_new_samples,)
         Indices of the samples selected.