  We analyze here the possibility of studying mass composition in the Auger
data sample using neural networks as a diagnostic tool. Extensive air showers
were simulated using the AIRES code, for the two hadronic interaction models in
current use: QGSJet and Sibyll. Both, photon and hadron primaries were
simulated and used to generate events. The output parameters from the ground
array were simulated for the typical instrumental and environmental conditions
at the Malarg\"ue Auger site using the code SAMPLE. Besides photons, hydrogen,
helium, carbon, oxygen, magnesium, silicon, calcium and iron nuclei were also
simulated. We show that Principal Components Analysis alone is enough to
separate individual photon from hadron events, but the same technique cannot be
applied to the classification of hadronic events. The latter requires the use
of a more robust diagnostic. We show that neural networks are potentially
powerful enough to discriminate proton from iron events almost on an
event-by-event basis. However, in the case of a more realistic multi-component
mixture of primary nuclei, only a statistical estimate of the average mass can
be reliably obtained. Although hybrid events are not explicitly simulated, we
show that, whenever hybrid information in the form of $X_{max}$ is introduced
in the training procedure of the neural networks, a considerable improvement
can be achieved in mass discrimination analysis.
