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This paper describes the results of experimental investigation of two-feature evaluation criteria, i.e., inter-intra class distance ratio and information content measure. These two indirect statistical measures take into account higher order statistical redundancies among the feature being evaluated. The algorithms are first presented and then they are applied and compared to recognize handprinted alphanumeric characters. Both Highleyman's data and raw data obtained in the Signal Processing Laboratory at Case Western Reserve University, Cleveland, Ohio, were used for the study. It is believed that the criteria can be used for other applications and can especially be used where the statistical independency among features is not assumed.