HMM has been largely applied in many fields with great success. To achieve a better performance, an easy way is using more states or more free parameters for a better signal modelling. Thus, state sharing and state clipping methods have been proposed to reduce parameter redundancy and to limit the explosive consummation of system resources. We focus on a simple state sharing method for a hybrid neuro-Markovian on-line handwriting recognition system. At first, a likelihood-based distance is proposed for measuring the similarity between two HMM state models. Afterwards, a minimum quantification error aimed hierarchical clustering algorithm is also proposed to select the most representative models. Here, models are shared to the most under the constraint of the minimum system performance loss. As the result, we maintain about 98% of the system performance while about 60% of the parameters are reduced.