  A Feed Forward Error Back Propagation Artificial Neural Network(ANN)
algorithm is developed for electron/positron identification in a wide momentum
region (10 - 300 GeV/c). The method was proposed for the Transition Radiation
Detector of the E781 experiment at Fermilab. The package consists of two parts:
  - the program for the ANN training;
  - the particle classification subroutine.
  Both parts are built using the object oriented technique and C++ language.
The particle identification algorithm is wrapped in FORTRAN closers to be used
in the E781 off-line program. The package performance was tested in comparison
with the likelihood ratio method using Monte Carlo generated data. Our study
has demonstrated the excellent ability of the ANN to learn even small details
of the detector response function. The ANN solution gives the same performance
and behavior as the likelihood method when using Monte Carlo data with known
detector parameters. It demonstrates that if trained with experimental data the
package can provide a very good solution to the classification problem of
$e^+/e^-$ tracks.
