Reading is a complex process that draws on a remarkable number of diverse perceptual and cognitive processes. In this review, I provide an overview of computational models of reading, focussing on models of visual word recognition-how we recognise individual words. Early computational models had 'toy' lexicons, could simulate only a narrow range of phenomena, and frequently had fundamental limitations, such as being able to handle only four-letter words. The most recent models can use realistic lexicons, can simulate data from a range of tasks, and can process words of different lengths. These models are the driving force behind much of the empirical work on reading. I discuss how the data have guided model development and, importantly, I also provide guidelines to help interpret and evaluate the contribution the models make to our understanding of how we read.