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This work describes a hand gesture recognition system using an optimized image processing-fuzzy C-means (FCM) algorithm. The parameters of the image processing and clustering algorithm were simultaneously found using a neighborhood parameter search routine, resulting in solutions within 1-2% of optimal. Comparison of user dependent and user independent systems, when tested with their own trainers, resulted in recognition accuracies of 98.9% and 98.2%, respectively. For experienced users, the opposite was true, testing recognition accuracies where better for user independent than user dependent systems (98.2% over 96.0%). These results are statistically significant at the .007 levels.