This paper provides a method to control a robotic arm by analyzing Electromyogram (EMG) collected from surface of the forearm. The electrodes are strategically placed on top of a particular muscle to harvest its myoelectric signals generated by physiological variations associated with muscular activities such as contraction and retraction. Since the movements of other muscles detectable under the electrodes are electrically superimposed, the main strategy in controlling motor units is to isolate the desired signals by filtering, sampling, and signal processing. The prototype design, incorporated into Field Programmable Gate Array (FPGA), is a low-cost and adaptable system that could potentially be available to any subject with a very minimal learning curve. The system yields quick responses and is scalable and capable of being a valuable tool for biomedical research and development.