Human-machine interfaces have not yet advanced to enable intuitive control of multiple degrees of freedom as offered by modern myoelectric prosthetic hands. Pattern Recognition (PR) control has been proposed to make human-machine interfaces in myoelectric prosthetic hands more intuitive, but it requires the user to generate high-quality, i.e., consistent and separable, electromyogram (EMG) patterns. To generate such patterns, user training is required and has shown promising results. However, how different levels of feedback affect effectivity in training differently, has not been established yet. Furthermore, a correlation between qualities of the EMG patterns (the focus of training) and user performance has not been shown yet. In this study, 37 able-bodied participants (mean age 21 years, 19 males) were recruited and trained PR control over five days. Three levels of feedback were tested for their effectiveness: no external feedback, visual feedback and visual feedback with coaching. Training resulted in improved performance from pre- to post-test with no interaction effect of feedback. Feedback did however affect the quality of the EMG patterns where people who did not receive external feedback generated higher amplitude patterns. A weak correlation was found between a principal component, composed of EMG amplitude and pattern variability, and performance. Our results show that training is highly effective in improving PR control regardless of feedback and that none of the quality metrics correlate with performance. We discuss how different levels of feedback can be leveraged to improve PR control training.