Hidden Markov Models (HMMs) have become within a few years the main technology for on line handwritten word recognition (HWR). We consider here segment models which generalize HMMs, these models aim at modeling the signal at a global level rather than at the frame level and have been shown to overcome standard HMMs in their modeling ability. We propose a new segment model, which allows to automatically handling different writing styles. We compare our system on the isolated character set of the UNIPEN database with a reference system and a baseline segment model.