Scalability and complexity of HPC software requires corresponding support from program analysis tools capable of dealing with possibly huge amounts of data. E.g., MPI traces capture low-level communication behavior essential to understand program behavior, but are typically too detailed and large and hence need to be reduced, pre-processed, and mapped to a higher level of abstraction before being comprehensible by human users. One possible approach is to utilize the occurrence of patterns in the execution of a program for the benefits of the user's understanding. Such patterns can be either user-defined or automatically extracted from program traces, requiring minimal interaction from users. This poster describes such an enhanced approach to pattern detection in program traces, which is available within a program analysis framework. In addition, the poster reports about patterns in existing, realistic applications and the pattern detection mechanism to extract useful information about a program's behavior.