An important parameter for building a cursive script model is the number of different, relevant letter writing variants. An algorithm performing this task automatically by optimizing the number of letter models in an HMM-based script recognition system is presented. The algorithm iteratively modifies selected letter models; for selection, quality measures like HMM distance and emission weight entropy are developed, and their correlation with recognition performance is shown. Theoretical measures for the selection of overall model complexity are presented, but best results are obtained by direct selection criteria: likelihood and recognition rate of training data. With the optimized models, an average improvement in recognition rate of up to 5.8 percent could be achieved.