Workflow management is more and more popular and its technologies are becoming more and more mature. However, an important feature that is essential to many business process are left unsolved ¡V namely the unexpected exception handling. Not only does unexpected exceptions degrade the performance of WFMS, it also reflects the defects of workflow design. Our research proposes a framework that uses case-based reasoning to find exception handling rules from historical exception instances. These exception handling rules can help both improving exception handling performance and enabling workflow evolution. Our framework includes an exception taxonomy, an exception case base, and a set of exception naïve models. When an exception occurs, it is classified into a particular category according to the exception taxonomy. Within the category a number of attributes are compared and a naïve model that best represents the incoming exception is identified. It is then adapt to the current environment for an appropriate handling approach. We discuss in the thesis how to discover naïve models from a set of exception instances. A case is finally studied to demonstrate the feasibility of our framework and address the issues of some subtle considerations.