Classifier combination is a powerful strategy to support useful solutions in difficult classification problems. Notwithstanding, the effectiveness of a multi-classifier system strongly depends on the decision fusion strategies. In this field, one of the most significant aspects concerns output normalization, when classifiers decisions are provided at measurement level. This paper presents a new approach for output normalization that uses Dynamic Time Warping (DTW). Some experimental tests have been carried out in the field of handwritten digit recognition. The proposed approach is superior to other output normalization algorithms in the literature.