A system for off-line cursive script recognition is presented. A new normalization technique (based on statistical methods) to compensate for the variability of writing style is described. The key problem of segmentation is avoided by applying a sliding window on the handwritten words. A feature vector is extracted from each frame isolated by the window. The feature vectors are used as observations in letter-oriented continuous density HMMs that perform the recognition. Feature extraction and modeling techniques are illustrated. In order to allow the comparison of the results, the system has been trained and tested using the same data and experimental conditions as in other published works. The performance of the system is evaluated in terms of character and word (with and without lexicon) recognition rate. Results comparable to those of more complex systems have been achieved.