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Although the human hand is a complex biomechanical system, only a small set of features may be necessary for observation learning of functional grasp classes. We explore how to methodically select a minimal set of hand pose features from optical marker data for grasp recognition. Supervised feature selection is used to determine a reduced feature set of surface marker locations on the hand that is appropriate for grasp classification of individual hand poses. Classifiers trained on the reduced feature set of five markers retain at least 92% of the prediction accuracy of classifiers trained on a full feature set of thirty markers. The reduced model also generalizes better to new subjects. The dramatic reduction of the marker set size and the success of a linear classifier from local marker coordinates recommend optical marker techniques as a practical alternative to data glove methods for observation learning of grasping.