A novel adaptive resonance theory (ART) neural network architecture is being proposed. The new model, called Hypersphere ART (H-ART) is based on the same principals like Fuzzy-ART does and, thus, inherits most of its qualities for unsupervised learning. Among these properties is fast, stable, incremental learning on the training set and good generalization on the testing set. While H-ART is intended for clustering tasks, its extension, H-ARTMAP is playing the role of Fuzzy-ARTMAP's counterpart for the supervised learning of real-valued, multi-dimensional mappings. In addition, in this paper, some experimental results are presented involving the comparison of H-ARTMAP and Fuzzy-ARTMAP in simple, illustrative classification problems. The results are indicating comparable performances in error rate but also a good potential for substantial superiority of H-ARTMAP in terms of nodes (categories) utilized. The latter effect can be attributed to H-ART's more efficient internal knowledge representation.