Currently available upper-limb, myoelectric prostheses usually accomplish gesture differentiation by analyzing myoelectric signals from multiple pairs of surface electrodes distributed over the user’s upper body. Another common control method is to use a single pair of surface electrodes but to enlist an application on a mobile device for specific gesture and grip settings. Both of these approaches limit the accessibility of the finished product because of their high cost and complexity. In our research, we attempt to increase the usability of myoelectric prostheses by increasing the amount of information that is extracted from the signal captured by a single pair of surface electrodes. We have designed and constructed an experimental apparatus that allows us to extract time and frequency domain content from myoelectric signals. Using this apparatus, we analyzed signals that were generated on the user’s forearm when a series of different hand gripping forces were applied. We also analyzed the signals that were generated when an array of different hand gestures were performed. Our results suggest that it is feasible to differentiate between gripping forces using a simple and rapid signal analysis but that such an analysis cannot be used to differentiate between different hand gestures. Future work will explore the use of more advanced signal analysis techniques to perform gesture differentiation.