  We have studied the performance of a new algorithm for electron/pion
separation in an Emulsion Cloud Chamber (ECC) made of lead and nuclear emulsion
films. The software for separation consists of two parts: a shower
reconstruction algorithm and a Neural Network that assigns to each
reconstructed shower the probability to be an electron or a pion. The
performance has been studied for the ECC of the OPERA experiment [1].
  The $e/\pi$ separation algorithm has been optimized by using a detailed Monte
Carlo simulation of the ECC and tested on real data taken at CERN (pion beams)
and at DESY (electron beams). The algorithm allows to achieve a 90% electron
identification efficiency with a pion misidentification smaller than 1% for
energies higher than 2 GeV.
