This thesis is about the use of EvolutionaryAlgorithms to design a better prosthetic hand controller. One of thegoals is to use methods that are easy to implement in a small, low-powerand low-cost system. The data set used is typical of the data thatwould be available in a real-world prosthesis. It was collected byKajitani at the National Institute of Advanced Industrial Scienceand Technology (AIST) from a person who had lost a hand, and no advancedpreprocessing of the signal was done. Evolutionary Algorithms areused to evolve a digital circuit which can predict the intended handmotion from the data presented to it. The data set is then analyzedto determine the factors that limit the successful classificationof signals. The maximum classification rate attainable is determined,and the expected maximum real-word performance is also evaluated.Finally, a method is found that improves the average classificationrate at the cost of increased response time. Compared to another workusing the same data set, the average classification rate for the testingdata rose from 55.1\% to 71.2\%, for the training data it rose from73.1\% to 92.3\%.