This paper presents a data-driven method to track hand gesture and animate 3D hand model. The proposed method uses a new generation of active camera based on time of flight principle(The Swissranger4000). To achieve the tracking, the presented method exploits a database of hand gestures represented as 3D point clouds acquired from the Swissranger4000 video camera. In order to track a large number of hand poses with a database as small as possible we classify the hand gestures using a Principal Component Analysis (PCA). Applied to each point cloud, the PCA produces a new representation of the hand pose independent of the position and orientation in the 3D space. To explore fast and efficiently the database we use a comparison function based on 3D distance transform. Experimental results on real data demonstrate the potential of the method