In this paper we propose a novel scheme to combine offline and online features of handwritten strokes. The stateof- the-art methods in handwritten stroke recognition have used a pre-determined combination of these features, which is not optimal in all situations. The proposed model addresses this issue by learning mixtures of offline and online characteristics from a set of exemplars. Each stroke is represented as a probabilistic sequence of substrokes with varying compositions of these features. The model adapts to any stroke and chooses the feature composition that best characterizes it. The superiority of the method is demonstrated on handwritten numeral and character strokes.