A methodology of combining HMM (hidden Markov model) and MLP (multi-layer perceptron) for cursive word recognition is presented in this paper. We have designed an explicit segmentation based HMM, and combined it with an implicit segmentation based MLP using weighting coefficients. The main idea of this methodology is that classifiers that are more distinct can better complement each other. We also introduced a new probability measure for the hybrid classifier as well as conventional combining schemes. Experiments were conducted with month word and legal word databases of CENPARMI and improved performances of 87.3% for 21-month word classes and 92.2% for 32 legal word classes have been achieved.