The research described in this paper analyzes the in-plane rotational robustness of the Viola-Jones object detection method when used for hand appearance detection. We determine the rotational bounds for training and detection for achieving undiminished performance without an increase in classifier complexity. The result - up to 15/spl deg/ total - differs from the method's performance on faces (30/spl deg/ total). We found that randomly rotating the training data within these bounds allows for detection rates about one order of magnitude better than those trained on strictly aligned data. The implications of the results effect both savings in training costs as well as increased naturalness and comfort of vision-based hand gesture interfaces.