To match automatically rotated stellar images, a rotation invariant matching method based on invariant feature descriptors was proposed, in which the Speeded Up Robust Features (SURF) was used to describe and match star features for the first time. First, a stellar image was segmented, and the non-maxima value was suppressed to extract star points in the stellar image. Then, a star distribution scale factor was calculated, the dominant orientation was obtained in a circle region with a radius of 6s, and the 20s×20s local region was rotated to the dominant orientation. In the local region, the SURF descriptor was calculated for each star. Finally, an automatic matching strategy based on the difference between dominant orientations was proposed. By this method, the threshold was calculated automatically and the transform matrix was given. Experimental results demonstrate that the proposed method can robustly detect star features and achieve a high precision stellar image matching between images with rotation, translation and perspective change. Obtained results show that correspondent star errors is below 1 pixel and 15 pixel for simulation and real image experiments, respectively. It indicates that the method to apply SURF descriptor to star matching and recognition is feasible.