A novel shape description which can be generally applied to both contour shape and region shape recognition is proposed in this study. This method treats the edge (including the inner edge) of the object as an unordered point-set, a hierarchical description model is built by iteratively partitioning the edge of the object into progressively smaller parts along different directions. At each level of the hierarchy structure, the geometrical features of the object edge are characterized by two measurements, partition ratio, and dispersion degree. Combining them, a hierarchical description of the object shape can then be constructed. The dissimilarity of two shapes can be measured by computing the L-1 distance between their hierarchical shape descriptors. The merits of the proposed method can be summarized as follows. (1) Both contour shape and region shape can be effectively described by this method, thus it has the ability for general use. (2) Based on the proposed hierarchical description framework, besides the proposed two measures, partition ratio, and dispersion degree, many other measures can be included for meeting various accuracy requirements on shape recognition, so the proposed method has extendibility. (3) The proposed hierarchical description scheme make the available descriptors characterize the shape from coarse to fine, so the proposed descriptor is multi-scale. (4) Instead of using all the pixel points of the object, the proposed method only take the edge points of the object into account, for this reason, it has a relative low computational complexity. Two standard test sets of MPEG-7 CE-2 region shape database and MPEG-7 CE-1 contour shape database are used to evaluate the performance of the proposed method. The experimental results indicate that the proposed method outperforms the state-of-the-art approaches in terms of a comprehensive consideration on the retrieval rates, retrieval efficiency, and general application ability.