Visual vocabularies are standard tools in the object/image classification literature, and are emerging as a new tool for building point correspondences for pose estimation. This paper proposes several visual word based methods for point matching, with structure from motion and pose estimation applications in view. The three dimensional geometry of a scene is first extracted with bundle adjustment techniques based on the keypoint correspondences. These correspondences are obtained by grouping the set of all SIFT descriptors from the training images into visual words. We obtain a more accurate 3D geometry than with classical image-to-image point matching. In the second step, these visual words serve as 3D point descriptors robust to viewpoint change, and are then used for building 2D-3D correspondences for a test image, yielding the pose of the camera by solving the PnP problem. We compare several visual word formation techniques w.r.t robustness to viewpoint change between the learning and test images and discuss the required computational time.