  We will show an application of neural networks to extract information on the
structure of hadrons. A Monte Carlo over experimental data is performed to
correctly reproduce data errors and correlations. A neural network is then
trained on each Monte Carlo replica via a genetic algorithm. Results on the
proton and deuteron structure functions, and on the nonsinglet parton
distribution will be shown.
