  We present a new method to extract parton distribution functions from high
energy experimental data based on a specific type of neural networks, the
Self-Organizing Maps. We illustrate the features of our new procedure that are
particularly useful for an anaysis directed at extracting generalized parton
distributions from data. We show quantitative results of our initial analysis
of the parton distribution functions from inclusive deep inelastic scattering.
