The design of a panel to identify target cell subsets in flow cytometry can be difficult when specific markers unique to each cell subset do not exist, and a combination of parameters must be used to identify target cells of interest and exclude irrelevant events. Thus, the ability to objectively measure the contribution of a parameter or group of parameters toward target cell identification independent of any gating strategy could be very helpful for both panel design and gating strategy design. In this article, we propose a discriminative information measure evaluation (DIME) based on statistical mixture modeling; DIME is a numerical measure of the contribution of different parameters towards discriminating a target cell subset from all the others derived from the fitted posterior distribution of a Gaussian mixture model. Informally, DIME measures the "usefulness" of each parameter for identifying a target cell subset. We show how DIME provides an objective basis for inclusion or exclusion of specific parameters in a panel, and how ranked sets of such parameters can be used to optimize gating strategies. An illustrative example of the application of DIME to streamline the gating strategy for a highly standardized carboxyfluorescein succinimidyl ester (CFSE) assay is described.