Improvements in the accuracy of repositioning patients in medical imaging systems during repeat examinations will allow a more precise measurement of the progress of the disease or treatment. The alignment task is normally carried out using a number of inadequate techniques varying from stereotatic frames, to a rigorous anatomical study of the organs shown in the different views. Present techniques are either unreliable or need total patient collaboration for the surgical implantation of a localising device. The paper describes a knowledge based approach, which will enable optimal matching to be achieved between the two data sets and will be able, at least in the case of MRI images, to provide the appropriate co-ordinates for an optimised new slice angle. An extension of the use of accurate repositioning would be the ability to cross match the different types of information from other imaging systems. The system will eventually be able to quantify the absolute difference between images subject to morphological change and temporal distortion. The method of approach uses an anatomical knowledge base to guide the segmentation of the scene into a number of clearly identified invariant key objects. The matching will proceed by iterating to progressively smaller features. Matching is carried out using symbolic feature spaced descriptions of the objects. Key words: Image matching, high level reasoning, stereotatic frames, MRI.