We describe a new approach to modelling the appearance of structures in grey-level images. We assume that both the shape and grey-levels of the structures can vary from one image to another, and that a number of example images are available for training. A 2-D image can be thought of as a surface in 3 dimensions, with the third dimension being the grey-level intensity at each image point. We can represent the shape of this surface by planting landmark points across it. By examining the way such collections of points vary across different examples we can build a statistical model of the shape, which can be used to generate new examples, and to locate examples of the modelled structure in new images. We show examples of these composite appearance models and demonstrate their use in image interpretation.