One of the major unsolved problems in knowledge acquisition is reconciling knowledge originating from different sources. This paper proposes a technique for reconciling knowledge in two independent knowledge bases, describes a working program built to implement that technique, and discusses an exploratory study for validating the technique. The technique is based on the use of heuristics for identifying and resolving discrepancies between the knowledge bases. Each heuristic developed provides detection and resolution procedures for a distinct variety of discrepancy in the knowledge bases. Same pie discrepancies include using synonyms for the same term, conflicting rules, and extra reasoning steps. Discrepancies are detected and resolved through the use of circumstantial evidence available from the knowledge bases themselves and by asking sharply focussed questions to the experts responsible for the knowledge bases. The technique was tested on two independently developed knowledge bases designed to aid novice statisticians in diagnosing problems in linear regression models. The heuristics located a significant number of the discrepancies between the knowledge bases and assisted the experts in creating a consensus knowledge base for diagnosing multicollinearity problems. We argue that the task of identifying discrepancies between independent bodies of knowledge is an inevitable part of any large knowledge acquisition effort. Hence the heuristics developed in this work are applicable even when knowledge acquisition is not done by reconciling two complete knowledge bases. We also suggest that our approach can be extended to other knowledge representations such as frames and database schemas. and speculate about its potential application to other domains involving the reconciliation of knowledge, such as requirements determination, negotiation, and design.