  An optimal choice of proper kinematical variables is one of the main steps in
using neural networks (NN) in high energy physics. Our method of the variable
selection is based on the analysis of a structure of Feynman diagrams
(singularities and spin correlations) contributing to the signal and background
processes. An application of this method to the Higgs boson search at the
Tevatron leads to an improvement in the NN efficiency by a factor of 1.5-2 in
comparison to previous NN studies.
