A boosting and pruning system and method for utilizing a plurality of neural networks, preferably those based on adaptive resonance theory (ART), in order to increase pattern classification accuracy is presented. The method utilizes a plurality of N randomly ordered copies of the input data, which is passed to a plurality of sets of booster networks. Each of the plurality of N randomly ordered copies of the input data is divided into a plurality of portions, preferably with an equal allocation of the data corresponding to each class for which recognition is desired. The plurality of portions is used to train the set of booster networks. The rules generated by the set of booster networks are then pruned in an intra-booster pruning step, which uses a pair-wise Fuzzy AND operation to determine rule overlap and to eliminate rules which are sufficiently similar. This process results in a set of intra-booster pruned booster networks. A similar pruning process is applied in an inter-booster pruning process, which eliminates rules from the intra-booster pruned networks with sufficient overlap. The final, derivative booster network captures the essence of the plurality of sets of booster networks and provides for higher classification accuracy than available using a single network.