Artificial Neural Networks are gaining recognition among High Energy Physicists as an effective means for particle identification in background-dominated environments. We have investigated the possibility of employing neural networks for identification of electrons and tau leptons with the D0 detector in the upcoming run of the Tevatron collider at Fermilab. Preliminary results based on Monte Carlo simulations indicate that for any acceptable level of signal efficiency, neural networks consistently outperform covariance matrices so far employed for the same purpose. Using a subset of variables used by a covariance matrix, a properly trained neural network offers 2 times better background rejection for taus, and 6 times for electrons, at 90% signal efficiency. Similar enhancements can be expected for identification of other objects (such as muons, b or c jets, quark vs gluon jets, neutrinos etc). Neural Networks may thus help improve many critical measurements, facilitate searches for new particles, and open doors to certain studies previously thought impossible because of excessive backgrounds.