We explore the feasibility of muscle-computer interfaces: an interaction methodology that directly senses and decodes human muscular activity rather than relying on physical device actuation or user actions that are externally visible or audible. As a first step towards realizing the muscle-computer interface concept, we conducted an experiment to explore the potential of exploiting muscular sensing and processing technologies for muscle-computer interfaces. We present results demonstrating accurate gesture classification with an off-the-shelf electromyography (EMG) device. Specifically, using 10 sensors worn in a narrow band around the upper forearm, we were able to differentiate position and pressure of finger presses, as well as classify tapping and lifting gestures across all five fingers. We discuss the implications of our results for future muscle-computer interface designs.