Hidden Markov Models have been successfully employed in speech recognition and, more recently, in sign language interpretation. They seem adequate for visual recognition of gestures. In this paper, two problems often eluded are considered. We propose to use the Bayesian information criterion in order to determine the optimal number of models states. We describe the contribution of continuous models in opposite of symbolic ones. Experiments on handwriting gestures show recognition rate between 88% and 100%.