  We introduce the neural network approach to the parametrization of parton
distributions. After a general introduction, we present in detail our approach
to parametrize experimental data, based on a combination of Monte Carlo methods
and neural networks. We apply this strategy first in three different cases: the
proton structure function, hadronic tau decays and B meson decay spectra.
Finally we describe the neural network approach applied to the parametrization
of parton distribution functions, and present results on the nonsinglet parton
distribution.
