Flow Cytometry is a process by which cells, and other microscopic particles, can be identified, counted, and sorted mechanically through the use of hydrodynamic pressure and laser-activated fluorescence labeling. As immunostained cells pass individually through the flow chamber of the instrument, laser pulses cause fluorescence emissions that are recorded digitally for later analysis as multidimensional vectors. Current, widely adopted analysis software limits users to manual separation of events based on viewing two or three simultaneous dimensions. While this may be adequate for experiments using four or fewer colors, advances have lead to laser flow cytometers capable of recording 20 different colors simultaneously. In addition, mass-spectrometry based machines capable of recording at least 100 separate channels are being developed. Analysis of such high-dimensional data by visual exploration alone can be error-prone and susceptible to unnecessary bias. Fortunately, the field of Data Mining provides many tools for automated group classification of multi-dimensional data, and many algorithms have been adapted or created for flow cytometry. However, the majority of this research has not been made available to users through analysis software packages and, as such, are not in wide use.