Case-based reasoning (CBR) has become an established part of artificial intelligence, both as a means for addressing fundamental AI problems and as a basis for fielded AI technology. This talk delineates the fundamental principles of CBR, examines how it is applied, and provides a synopsis of key needs for the next generation of CBR systems. It first presents an overview of the case-based reasoning process, clarifying the relationship of CBR to other AI methods, the motivations for the case-based approach, and fundamental issues for CBR methods. It illustrates this discussion with examples of CBR systems that reason and learn in multiple task domains. It closes with lessons learned from CBR research and applications, current challenges for case-based reasoning, and opportunities for the next generation of CBR systems and the future impact of CBR.