A framework for integrating methods for decision support; Case-Based Reasoning (CBR) and Data Mining (DM) is outlined. The integration approaches are divided according to which method that is considered to be mas~er and which is the slave. A system using Bayesian networks for computing similarity metrics is implemented and compared to a traditional CBR system. Data are taken from a database from the oil industry. The retrieved cases vary greatly between the systems, especially on features that are unspecified in the "new case". If many features of the "new case" are specified, the new system performs better, according to an evaluation by a domain expert.