The paper tackles the problem of feature points matching between pair of images of the same scene. This is a key problem in computer vision. The method we discuss here is a version of the SVD-matching proposed by Scott and Longuet-Higgins and later modified by Pilu, that we elaborate in order to cope with large scale variations. To this end we add to the feature detection phase a keypoint descriptor that is robust to large scale and view-point changes. Furthermore, we include this descriptor in the equations of the proximity matrix that is central to the SVD-matching. At the same time we remove from the proximity matrix all the information about the point locations in the image, that is the source of mismatches when the amount of scene variation increases. The main contribution of this work is in showing that this compact and easy algorithm can be used for severe scene variations. We present experimental evidence of the improved performance with respect to the previous versions of the algorithm.