Due to the critical nature of knowledge base applications in important intelligence-based environments, much more stringent standards have to be imposed now on their ability to provide reliable decisions in an accurate manner. It is our contention that in order to build reliable knowledge-based systems, it is important that the knowledge in the system be suitably abstracted, structured, and otherwise clustered in a manner which facilitates its understanding, verification, validation, maintenance, management and testing. The MVP-CA methodology addresses partitioning rule-based systems into a number of meaningful units before attempting the above activities. Pragati’s Multi-ViewPoint-Clustering Analysis (MVP-CA) tool provides such a framework for clustering large, homogeneous knowledge-based systems from multiple perspectives. It is a semi-automated tool allowing the user to focus attention on different aspects of the problem, thus providing a valuable aid for comprehension, maintenance, integration and evolution of knowledge-based systems.