There is an extensive literature on image models; perhaps the greatest emphasis has been on models based on Markov random fields. This article briefly discusses some general aspects of image modeling:what should be modeled (the scene rather than the image; both the ‘objects' and the ‘clutter’); the importance of non-standard types of models; the oversimplification of conventional models; possible limitations on the effectiveness of realistic models; models in higher-dimensional domains; and general guidelines for selecting problems to which models can be effectively applied.