There are many methodologies for performing gene expression profiling on transcripts, and through their use scientists have been generating vast amounts of experimental data. Turning the raw experimental data into meaningful biological observation requires a number of processing steps; to remove noise, to identify the “true” expression value, normalize the data, compare it to reference data, and to extract patterns, or obtain insight into the underlying biology of the samples being measured. In this chapter we give a brief overview of how the raw data is processed, provide details on several data-mining methods, and discuss the future direction of expression informatics.