  A neural network method is developed to discriminate direct photons from the
neutral pion background in the PHOS spectrometer of the ALICE experiment at the
LHC collider. The neural net has been trained to distinguish different classes
of events by analyzing the energy-profile tensor of a cluster in its eigen
vector coordinate system. Monte-Carlo simulations show that this method
diminishes by an order of magnitude the probability of $\pi^0$-meson
misidentification as a photon with respect to the direct photon identification
efficiency in the energy range up to 120 GeV.
