Interactive software systems for pattern analysis and recognition represent powerful scientific, researching, and development tools for designing pattern recognition systems. In this paper, we discuss one such software system named PC-PARIS (Pattern Analysis and Recognition Interactive System for PC compatible computers). The variety of pattern recognition and clustering algorithms, implemented in PC-PARIS, are briefly described. As application examples, we firstly present a procedure for the optimal pattern classifier design for the classification of image data using this program. Also, we present an analysis of the sensitivity of statistical pattern classifiers, implemented in PC-PARIS, to the increasing number of features in limited training data set conditions, for synthesized and real data classification cases.