The “one against one” and the “one against all” are the two most popular strategies for multi-class SVM; however, according to the literature review, it seems impossible to conclude which one is better for handwriting recognition. Thus, we compared these two classical strategies on two different handwritten character recognition problems. Several post-processing methods for estimating posterior probability were also evaluated and the results were compared with the ones obtained using MLP. Finally, the “one against all” strategy appears significantly more accurate for digit recognition, while the difference between the two strategies is much less obvious with upper-case letters. Besides, the “one against one” strategy is substantially faster to train and seems preferable for problems with a very large number of classes. To conclude, SVMs allow significantly better estimation of probabilities than MLP, which is promising from the point of view of their incorporation into handwriting recognition systems.