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Amount of software engineering data that is accumulated by software companies grows with enormous speed. This data is a source of knowledge about different activities related to software development and maintenance. Many different techniques and tools have been developed and proposed for extracting knowledge and representing it in forms understandable by people. These techniques are based on different principles and they process data differently. This paper illustrates a multi-technique approach to analysis of data. A detailed case study of analyzing software maintenance data is presented. Different models are built, analyzed and evaluated. The first model is a Bayesian network. The second is a set of IF-THEN rules extracted from the data, and the third one is built using a decision tree. The emphasis of the analysis is put on two aspects - how the models support understanding of a process represented by the data, and how good prediction capabilities these models have.