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Depending on the tracking technology in use, a 6D motion gesture can be tracked and represented explicitly by the position and orientation or implicitly by the acceleration and angular speed. In this work, we first present the reasoning for the definition and recognition of motion gestures. Five basic feature vectors are then derived from the 6D motion data. Our main contribution is to investigate the relative effectiveness of various feature dimensions for motion gesture recognition in both user dependent and user independent cases. We also propose a feature normalization procedure and prove its effectiveness in achieving “scale” invariance especially in the user independent case. Our study gives an insight into the attainable recognition rate with different tracking devices.