Data envelopment analysis (DEA) is a nonparametric method from the area of operations research that measures the relationship of produced outputs to assigned inputs and determines an efficiency score. This efficiency score can be interpreted as a performance measure in investment analysis. Recent literature contains intensive discussion of using DEA to measure the performance of hedge funds, as this approach yields some advantages compared to classic performance measures. This paper extends the current discussion in three aspects. First, we present different DEA models and analyze their suitability for hedge fund performance measurement. Second, we systematize possible inputs and outputs for DEA and again examine their suitability for hedge fund performance measurement. Third, two rules are developed to select inputs and outputs in DEA of hedge funds. Using this framework, we find a completely new ranking of hedge funds compared to classic performance measures and compared to previously proposed DEA applications. Thus, we propose that classic performance measures should be supplemented with DEA based on the suggested rules to fully capture hedge fund risk and return characteristics.