Modern, commercially available hand prostheses offer the potential of individual digit control. However, this feature is often not utilized due to the lack of a robust scheme for finger motion estimation from surface electromyographic (EMG) measurements. Regression methods have been proposed to achieve closed-loop finger position, velocity, or force control. In this paper, we propose an alternative approach, based on open-loop action-based control, which could be achieved through simultaneous finger motion classification. We compare the efficacy of continuous closed-loop and discrete open-loop control on the task of controlling the five degrees of actuation (DOAs) of a dexterous robotic hand. Eight normally-limbed subjects were instructed to teleoperate the hand using a data glove and the two control schemes under investigation in order to match target postures presented to them on a screen as closely as possible. Results indicate that, firstly, the performance of the two control methods is comparable and, secondly, that experience can lead to significant performance improvement over time, regardless of the method used. These results suggest that prosthetic finger control in a continuous space can be potentially achieved by means of myoelectric classification and discrete, action-based control and hence encourage further research in this direction.