Myoelectric pattern recognition control can potentially provide upper limb amputees with intuitive control of multiple prosthetic functions. However, the lack of robustness of myoelectric pattern recognition algorithms is a barrier for clinical implementation. One issue that can contribute to poor system performance is electrode shift, which is a change in the location of the electrodes with respect to the underlying muscles that occurs during donning and doffing and daily use. We investigated the effects of interelectrode distance and feature choice on system performance in the presence of electrode shift. Increasing the interelectrode distance from 2 cm to 4 cm significantly (p<;0.01) improved classification accuracy in the presence of electrode shifts of up to 2 cm. In a controllability test, increasing the interelectrode distance from 2 cm to 4 cm improved the user's ability to control a virtual prosthesis in the presence of electrode shift. Use of an autoregressive feature set significantly (p<;0.01) reduced sensitivity to electrode shift when compared to use of a traditional time-domain feature set.