We describe a method for learning statistical models of images using a second-order hidden Markov mesh model. First, an image can be segmented in a way that best matches its statistical model by an approach related to the dynamic programming used for segmenting Markov chains. Second, given image segmentation, a statistical model (3D state transition matrix and observation distributions within states) can be estimated. These two steps are repeated until convergence to provide both segmentation and a statistical model of the image. We propose a statistical distance measure between images based on the similarity of their statistical models, for classification and retrieval tasks.