  We present a signal-from-background separation study based on neural networks
technique applied to a W/quartz fiber calorimeter. Performance results in terms
of signal efficiency and improvement of the signal-to-background ratio are
presented. We conclude that by using neural networks we can efficiently
separate signal from background and achieve a signal enhancement over the
background of the order of several thousands at high efficiency.
