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Real-time pattern recognition control is frequently affected by misclassifications. This study investigated the use of a decision-based velocity ramp that attenuated movement speed after a change in classifier decision. The goal was to improve prosthesis positioning by minimizing the effect of unintended movements. Nonamputee and amputee subjects controlled a prosthesis in real time using pattern recognition. While performing a target achievement test in a virtual environment, subjects had a significantly higher completion rate (p <; 0.05) and a more direct path (p <; 0.05) to the target with the velocity ramp than without it. Using a physical prosthesis, subjects stacked a greater average number of 1-in cubes (p <; 0.05) in 3 min with the velocity ramp than without it (76% more blocks for nonamputees; 89% more blocks for amputees). Real-time control using the velocity ramp also showed significant performance improvements above using majority vote. Eighty-three percent of subjects preferred to control the prosthesis using the velocity ramp. These results suggest that using a decision-based velocity ramp with pattern recognition may improve user performance. Since the velocity ramp is a postprocessing step, it has the potential to be used with a variety of classifiers for many applications.