The adaptive resonance theory (ART) suggests a solution to the stability-plasticity dilemma facing designers of learning systems, namely how to design a learning system that will remain plastic, or adaptive, in response to significant events and yet remain stable in response to irrelevant events. ART architectures are discussed that are neural networks that self-organize stable recognition codes in real time in response to arbitrary sequences of input patterns. Within such an ART architecture, the process of adaptive pattern recognition is a special case of the more general cognitive process of hypothesis discovery, testing, search, classification, and learning. This property opens up the possibility of applying ART systems to more general problems of adaptively processing large abstract information sources and databases. The main computational properties of these ART architectures are outlined and contrasted with those of alternative learning and recognition systems.