The advent of mass cytometry has lead to an unprecedented increase in the number of analytes measured in individual cells, thereby increasing the complexity and information content of cytometric data. While this technology is ideally suited to detailed examination of the immune system, the applicability of the different methods for analyzing such complex data are less clear. Conventional data analysis by ‘manual’ gating of cells in biaxial dotplots is often subjective, time consuming, and neglectful of much of the information contained in a highly dimensional cytometric dataset. Algorithmic data mining has the promise to eliminate these concerns and several such tools have been recently applied to mass cytometry data. Herein, we review computational data mining tools that have been used to analyze mass cytometry data, outline their differences, and comment on their strengths and limitations. This review will help immunologists identify suitable algorithmic tools for their particular projects.