It is a common practice in the process industry to compress process data before it is archived. However, compression may alter the original data in a manner that makes extracting useful information from it more difficult. In this paper, popular data compression methods and their effect on pattern matching in historical data are evaluated. Pattern matching is performed using principal-component analysis based similarity factors. Simulation results indicate that wavelet-based compression provides the best compression for pattern matching, while compression using OSI PI software produces the best reconstruction of data.