The application of K-means (ISODATA) cluster analysis to flow cytometric data is described. The results of analyses of flow cytometric data for mixtures of fluorescent microspheres and samples of peripheral blood mononuclear cells are presented. A method for simultaneously displaying list mode data for any number of parameters, which had previously been applied to a continuous set of parameters such as multi-angle light scattering data, is used to present the results of cluster analysis on physically unrelated parameters; this method allows rapid evaluation of the success of subpopulation identification. The factors that influence automated identification of subpopulations are examined, and methods for determining optimal values for these factors are described.