The capability of flow cytometry to offer rapid quantification of multidimensional characteristics for millions of cells has made this technology indispensable for health research, medical diagnosis, and treatment. However, the lack of statistical and bioinformatics tools to parallel recent high-throughput technological advancements has hindered this technology from reaching its full potential. Traditional methods for flow cytometry (FCM) data processing have relied on manual gating of cell events to define cell populations for statistical analysis. However, this approach has become increasingly problematic with the advances in instrumentation and reagents that allow for evaluation of larger numbers of cell properties. During this talk, I will review some of the statistical issues involved in the analysis of flow cytometry data and some of the methods, based on model based clustering, that we have proposed to address these. I will also discuss the Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) competition that we and others have established a year ago. In the context of FlowCAP, a common set of FCM data together with manual gating results for comparative analysis was developed and made available for assessing and comparing algorithms. I will present preliminary results from the first competition, flowCAP I, and introduce the next competition flowCAP II.