People want to live independently, but too often disabilities or advanced age robs them of the ability to do the necessary activities of daily living (ADLs). Finding relationships between electromyograms measured in the arm and movements of the hand and wrist needed to perform ADLs can help address performance deficits and be exploited in designing myoelectrical control systems for prosthetics and computer interfaces. This paper reports on several machine learning techniques employed to discover the electromyogram patterns present when using the hand to perform 14 typical fine motor functional activities used to accomplish ADLs. Classification and clustering techniques are employed. Improvements to accuracies are introduced, including the use of exponential smoothing and using a symbolic representation to approximate signal streams. Results show the patterns can be learned to an accuracy of approximately 77% for a 15 class problem and the symbolic representation shows the potential for future improvement in accuracies.