Recognition of handwritten digits has been one of the first applications of neural networks. Efficient methods have already been proposed to solve this task. We propose an intermediate approach between classical methods, which are based on extraction of a small set of parameters, and pure neural methods,in which the neural network is fed with raw image data. Complexity and learning time are reduced with still good performances. On a data base of 2589 digits coming from 30 people, we provide experimental results and comparisons of various parameters and classifiers.