SIFT (Scale Invariant Feature Transform) is one of the most effective local feature of scale, rotation and illumination invariant, which is widely used in the field of image matching. While there will be a lot mismatches when an image has many similar regions. In this study, an improved SIFT feature matching algorithm with local shape context is put forward. The feature vectors are computed by dominant orientation assignment to each feature point based on elliptical neighboring region and with local shape context and then the feature vectors are matched by using Euclidean distance and the X ² distance. The experiment indicates that the improved algorithm can reduce mismatch probability and acquire good performance on affine invariance, improves matching results greatly.