Many organizations use business policies to govern their business processes, often resulting in huge amounts of policy documents. As new regulations arise such as Sarbanes-Oxley, these business policies must be modified to ensure their correctness and consistency. Given the large amounts of business policies, manually analyzing policy documents to discover process information is very time-consuming and imposes excessive workload. In order to provide a solution to this information overload problem, we propose a novel approach named Policy-based Process Mining (PBPM) to automatically extracting process information from policy documents. Several text mining algorithms are applied to business policy texts in order to discover process-related policies and extract such process components as tasks, data items, and resources. Experiments are conducted to validate the extracted components and the results are found to be very promising. To the best of our knowledge, PBPM is the first approach that applies text mining towards discovering business process components from unstructured policy documents. The initial research results presented in this paper will require more research efforts to make PBPM a practical solution. © Springer-Verlag 2009.