Abstract: A computer automated feature selection method based upon the evaluation of hyper-rectangles and the ability of these rectangles to discriminate between classes. The boundaries of the hyper-rectangles are established upon a binary feature space where each bit indicates the relationship of a real feature value to a boundary within the minimum and maximum values for the feature across all samples. Data reduction combines the binary vector spaces so that the number of samples within a single class is within a range which is computationally feasible. Identification of subclasses identifies maximal subsets of S+ which are exclusive against S−. Feature evaluation determines within a single subclass the contribution of each feature towards the ability to discriminate the subclass from S−. The base algorithm examines each feature, dropping any feature which does not contribute towards discrimination. A pair of statistics are generated for each remaining feature.