In common law jurisdictions, legal research often involves an analysis of relevant case law. Court opinions comprise several high-level parts with different functions. A statement's membership in one of the parts is a key factor influencing how the statement should be understood. In this paper we present a number of experiments in automatically segmenting court opinions into the functional and the issue specific parts. We defined a set of seven types including Background, Analysis, and Conclusions. We used the types to annotate a sizable corpus of US trade secret and cyber crime decisions. We used the data set to investigate the feasibility of recognizing the parts automatically. The proposed framework based on conditional random fields proved to be very promising in this respect. To support research in automatic case law analysis we plan to release the data set to the public.