Matching points between two or multiple images of a scene is a vital component in many computer vision and pattern recognition tasks. The key step of point matching is how to construct a distinctive and robust descriptor. A state-of-the-art scale-invariant feature transform (SIFT) descriptor has proven that it outperforms other local descriptors on the distinctiveness and robustness. However, the SIFT descriptor neglects the global context of the feature points, as thus it fails to resolve the ambiguities that occur in local similar regions in an image. In this paper, a spatial distribution (SD) descriptor is constructed for each feature point detected by the SIFT method. It uses a log-polar histogram to build the global component according to the difference-of-Gaussian convolution image information. The spatial distribution descriptor has rotation, zoom invariance and partial skewness invariance due to that it integrates the local and global information of feature points. Points matching are performed on various images by the proposed framework. Experimental results show that the SD method outperforms the method using only SIFT.