  The precise knowledge of the atmospheric neutrino fluxes is a key ingredient
in the interpretation of the results from any atmospheric neutrino experiment.
In the standard atmospheric neutrino data analysis, these fluxes are
theoretical inputs obtained from sophisticated numerical calculations based on
the convolution of the primary cosmic ray spectrum with the expected yield of
neutrinos per incident cosmic ray. In this work we present an alternative
approach to the determination of the atmospheric neutrino fluxes based on the
direct extraction from the experimental data on neutrino event rates. The
extraction is achieved by means of a combination of artificial neural networks
as interpolants and Monte Carlo methods for faithful error estimation
