A novel scheme for efficient shape classification using region descriptors and extreme learning machine with kernels is proposed. The skeleton and boundary of the input shape image are first extracted. Then the boundary is simplified to remove noise and minor variations. Finally, region descriptors for the local skeleton, and the simplified shape signature are constructed to form a hybrid feature vector. Training and classification are then performed using kernel extreme learning machine (k-ELM) for efficient shape classification. Experimental results show that the proposed scheme is very fast and can archive higher classification accuracy on the challenging MPEG-7 dataset, outperforming existing state-of-the-art methods.