  We present an alternative algorithm to global fitting procedures to construct
Parton Distribution Functions (PDFs) parametrizations. The proposed algorithm
uses Self-Organizing Maps (SOMs) which at variance with the standard Neural
Networks, are based on competitive-learning. SOMs generate a non-uniform
projection from a high dimensional data s pace onto a low dimensional one
(usually 1 or 2 dimensions) by clustering similar PDF representations together.
The SOMs are trained on progressively narrower selections of data samples. The
selection criterion is that of convergence towards a neighborhood of the
experimental data. All available data sets on deep inelastic scattering in the
kinematical region of 0.001 < x < 0.75, and 1 <Q^2 < 100 GeV^2, with a cut on
the final state invariant mass, W^2 > 10 GeV^2 were implemented. The proposed
fitting procedure, at variance with standard neural network approaches, allows
for an increased control of the systematic bias by enabling the user to
directly control the data selection procedure at various stages of the process.
