Hands are important anatomical structures for musical performance, and recent developments in input device technology have allowed rather detailed capture of hand gestures using consumer-level products. While in some musical contexts, detailed hand and finger movements are required, in others it is sufficient to communicate discrete hand postures to indicate selection or other state changes. This research compared three approaches to capturing hand gestures where the shape of the hand, i.e. the relative positions and angles of finger joints, are an important part of the gesture. A number of sensor types can be used to capture information about hand posture, each of which has various practical advantages and disadvantages for music applications. This study compared three approaches, using optical, inertial and muscular information, with three sets of 5 hand postures (i.e. static gestures) and gesture recognition algorithms applied to the device data, aiming to determine which methods are most effective.