The RVM-based learning method for whole body pose estimation proposed by Agarwal and Triggs is adapted to hand pose recovery. To help overcome the difficulties presented by the greater degree of self-occlusion and the wider range of poses exhibited in hand imagery, the adaptation proposes a method for combining multiple views. Comparisons of performance using single versus multiple views are reported for both synthesized and real imagery, and the effects of the number of image measurements and the number of training samples on performance are explored.