  In the present work we carry out a study of the high energy cosmic rays mass
identification capabilities of a hybrid detector employing both fluorescence
telescopes and particle detectors at ground using simulated data. It involves
the analysis of extensive showers with zenith angles above 60 degrees making
use of the joint distribution of the depth of maximum and muon size at ground
level as mass discriminating parameters. The correlation and sensitivity to the
primary mass are investigated. Two different techniques - clustering algorithms
and neural networks - are adopted to classify the mass identity on an
event-by-event basis. Typical results for the achieved performance of
identification are reported and discussed. The analysis can be extended in a
very straightforward way to vertical showers or can be complemented with
additional discriminating observables coming from different types of detectors.
