Abstract: A Feature Map and learning rule-based Classifier capability is demonstrated that maximizes performance of classifier decisions. The invention discriminates among a large set of cases to make the correct decision for each case and generalizes well by correctly deciding cases never seen before. The Feature Map capability consists of Kohonen Self Organizing Feature Map (SOFM) followed by a four layer Evolutionary Perceptron. During training, the SOFM learns a set exemplars, one for each case. In operation, the SOFM takes an input state vector and maps it into the closest exemplar. The Evolutionary Perceptron uses algorithms that evolve both the NN weights and sigma (step size) and has two hidden layers required to discriminate nonlinear class boundaries. A population of NNs is evolved from generation to generation by modifying weights and sigma values. The evolutionary programming approach in the classifier capabiliy is used to search through from most general to most specific rules.