In this study, we developed a modified maximum likelihood (ML) algorithm for efficient computation in implemeting the minimum classifcation error (MCE) like training for optimally estimating the state-dependent polynomial coefficients in the trended HMM. We devised a new discriminative training method which controls the in uence of outliers in the training data on the constructed models. The resulting models seem to provide correct recognition for confusable patterns. For alphabet recognition tasks, outlier emphasis resulted in improved performance. An error rate reduction of 14% is achieved for the linear trend and 7.5% is obtained for the constant trend models over the traditional ML training models.