A major challenge facing the field of neural prosthetics is the issue of obtaining stable access to a sufficient number of neural signals to allow users intuitive control of their prosthesis. Neuroprosthetic devices typically gain access to neural signals through a percutaneous connector. This percutaneous connector is susceptible to infection and increases the risk of the prosthesis user suffering additional limb trauma. Electromyographically (EMG) controlled prostheses using surface EMG electrodes avoid problems with infection and additional trauma; however, it can be difficult to obtain more than three or four independent control signals on a residual limb using surface EMG electrodes. In order to avoid the risks associated with percutaneous implants and determine the efficacy of implantable wireless EMG sensors, we examined whether wireless Implantable MyoElectric Sensors (IMES) could provide a stable long-term interface with high channel independence for EMG signals. We trained a monkey to perform randomly cued, individuated finger flexions of the thumb, index, and middle finger as well as multiple, combined finger movements. Nine IMES were then surgically implanted into the finger, wrist, and thumb flexors and extensors of a rhesus monkey. After more than 10 months the IMES are without any observable adverse effects. Using a wireless link that provided power and data transfer, we recorded EMG from the IMES as the monkey performed the finger flexion task. The recorded EMG was first low-pass filtered and passed through a nonlinear energy operator. The EMG surrounding switch closures was then clustered according to the principle components. The centroid of the projected EMG cluster was determined for each finger using a training data set. The algorithm then classified which finger switch was pressed based on the closest centroid to the projected EMG for each switch closure in the test data set. This algorithm correctly decoded which individual finger was pressed 89% of the time. We also wrote a blind version of this PCA based algorithm, which detects and classifies if a finger is flexed for each time instant. The results of this work demonstrate that IMES offer a safe and promising approach for providing intuitive, dexterous control of artificial limbs and hands after amputation.