The screening of catalysts with desired catalytic activity and selectivity for electrocatalytic fuel cell reactions is a time-consuming process. One approach to address this problem is to apply combinatorial analysis techniques. In this paper, we present the results of an investigation of the application of systematic statistical analysis techniques such as analysis of variance (ANOVA) analysis and regression modeling for the development of effective screening methods of bimetallic and trimetallic nanoparticle catalysts. Based on several sets of experimental data from the chosen catalysts, empirical models derived from statistical analysis techniques were first built to fit the experiment results for each of the electrocatalytic parameters such as catalytic peak current, peak potential, Tafel slope and mass activities. These parameters were expressed as a function of catalyst component proportion variables and process variables. The adequacy of the chosen models is verified with residual analysis. The catalyst properties were also analyzed using a response surface approach. The statistical analysis results from the available experiment data provided useful information to aid the understanding of the relationship between the catalyst activities and compositions, which may provide guidance for experimental design toward discovery of catalysts with desired activity and selectivity.