The ultimate goal of machine vision is image understanding - the ability not only to recover image structure but also to know what it represents. By definition, this involves the use of models which describe and label the expected structure of the world. Over the past decade, model- based vision has been applied successfully to images of man-made objects. It has proved much more difficult to develop model-based approaches to the interpretation of images of complex and variable structures such as faces or the internal organs of the human body (as visualised in medical images). In such cases it has even been problematic to recover image structure reliably, without a model to organise the often noisy and incomplete image evidence. The key problem is that of variability. To be useful, a model needs to be specific - that is, to be capable of representing only ’legal’ examples of the modelled object(s). It has proved difficult to achieve this whilst allowing for natural variability. Recent developments have overcome this problem; it has been shown that specific patterns of variability inshape and grey-level appearance can be captured by statistical models that can be used directly in image interpretation