Pattern recognition of myoelectric signals for the control of prosthetic devices has been widely reported and debated. A large portion of the literature focuses on offline classification accuracy of pre-recorded signals. Historically, however, there has been a semantic gap between research findings and a clinically viable implementation. Recently, renewed focus on prosthetics research has pushed the field to provide more clinically relevant outcomes. One way to work towards this goal is to examine the differences between research and clinical results. The constrained nature in which offline training and test data is often collected compared to the dynamic nature of prosthetic use is just one example. In this work, we demonstrate that variations in limb position after training can have a substantial impact on the robustness of myoelectric pattern recognition.