An improved Scale Invariant Feature Transform (SIFT) method was proposed to implement the fast object recognition under a multiple varying background. Firstly, the scale space of object image was established, SIFT feature points were extracted and classified by their sizes. Only by comparing the same kinds of feature points , the target recognition could be completed. Then, four new angles were computed from the sub-region orientation histogram to represent the orientation information of each SIFT feature. Meanwhile, the feature point matching range was limited according to angle information in the target recognition to improve the calculation speeds of the SIFT algorithm. Finally, the scale factor between object image and target image was calculated and the object feature points were matched under the constraint by the scale factor to increase the number of correct matches and to insure the robustness of object recognition. Object recognition experiments were operated under object external occlusions, object rotation, scale change and illumination conditions. Results show that improved SIFT method has better performance of object recognition, and its computation speed has raised more than 40% as comparing with that of original SIFT algorithm.