We try to incorporate a graphical model to solve the problem of object recognition, which is a fundamental problem in computer vision. Adopting the multiscale feature keypoint technique, we present an object recognition algorithm that establishes the center, scale factor, and rotation angle of the object in the images. First, the local invariant features are detected in template and scene images. Second, the belief propagation algorithm is used to compute the correspondence considering the spatial constraints. Third, each correspondence point records a vote to the object's center, scale factor, and rotation angle. Finally, we keep the densest point on the vote map as the recognition result. Experimental results demonstrate the robustness of the algorithm on real images.