Manual interaction tasks, such as playing a musical instrument, require certain amounts of training until users are proficient. Electromyography (EMG) can bridge this gap and is able to provide proficiency feedback without the need for supervision. EMG measures the electrical potential that is related to muscular activity and has been used in Human-Computer-Interaction (HCI) in a variety of applications. This thesis explores the usage of EMG together with domain-specific movements, such as playing guitar chords, in the context of musician-instrument interaction. This includes a review of related work, an evaluation of suitable features, and machine learning methods as well as the realization of an EMG guitar tutor system. The results of this thesis show that it is possible to classify guitar chords with an average F1-measure of 87\%. We identified a trade-off between classifier-accuracy and window size, which is an important finding regarding real-time interaction. Further, we evaluated a guitar tutor system within a study. The results suggest, that electrodes and wires did not limit the participants in playing the guitar. An analysis of inter-person generalizability shows that dimensionality reduction methods can slightly increase the classifier performance. We propose further solutions to enhance the guitar tutor system from a machine learning perspective as well as from an usability perspective. Ultimately, we discuss how our findings can be transferred to related domains.