This paper presents ongoing work on using data mining to discover knowledge about software systems thus facilitating program comprehension. We discuss how this work fits in the context of tool supported maintenance and comprehension and report on applying a new methodology on C++ programs. The overall framework can provide practical insights and guide the maintainer through the specifics of systems, assuming little familiarity with these. The contribution of this work is two-fold: it provides a model and associated method to extract data from C++ source code which is subsequently to be mined, and evaluates a proposed framework for clustering such data to obtain useful knowledge. The methodology is evaluated on three open source applications, results are assessed and conclusions are presented. This paper concludes with directions for future work.