Abstract: A neural network model replaces the supervised labeling component of a supervised learning system with an incremental cluster validity index-based unsupervised labeling component. An implementation is presented combining fuzzy adaptive resonance theory predictive mapping (ARTMAP) and incremental cluster validity indices (iCVI) for unsupervised machine learning purposes, namely the iCVI-ARTMAP. An iCVI module replaces the adaptive resonance theory (ART) module B of a fuzzy ARTMAP neural network model and provides assignments of input samples to clusters (i.e., labels) at each learning iteration in accordance to any of several possible iCVI methods described. A map field incrementally builds a many-to-one mapping of the categories of ART module A to the cluster labels. At the end of each learning epoch, clusters may be merged and/or split using the iCVI, which is recomputed incrementally except for the newly cluster during a split.