This chapter discusses that the theoretical and experimental work on concepts and categories is progressing quickly in a number of related disciplines, particularly in experimental psychology and within that area of computer science research dealing with machine learning. The goals of these two disciplines generally differ in basic ways, regardless of the topic. Machine learning models often set out to accomplish a practical engineering purpose, like evolving an expert system. Computer simulation models developed within this discipline typically test a set of ideas that is meant to explain human performance. In spite of this apparent discrepancy between these disciplines, historically both have influenced each other in a positive way. Work in cognitive/experimental psychology has set problems for machine learning research and has suggested new approaches. It focuses on the common ground-the issues and ideas-currently shared by both machine learning and experimental psychology on the topics of categories and concepts. The chapter discusses the reflect recent developments that are likely to have a continuing impact on current concepts and categories in both psychology and machine learning: the development and testing of connectionist categorization models, and a growing interest in how two factors-background knowledge and exposure to instances-contribute to category learning and processing.