Recognition of hand-drawn shapes is an important and widely studied problem. By adopting a generative probabilistic framework we are able to formulate a robust and flexible approach to shape recognition which allows for a wide range of shapes and which can recognize new shapes from a single exemplar. It also provides meaningful probabilistic measures of model score which can be used as part of a larger probabilistic framework for interpreting a page of ink. We also show how Bayesian model comparison allows the trade-off between data fit and model complexity to be optimized automatically.