  The possible application of boosted neural network to particle classification
in high energy physics is discussed. A two-dimensional toy model, where the
boundary between signal and background is irregular but not overlapping, is
constructed to show how boosting technique works with neural network. It is
found that boosted neural network not only decreases the error rate of
classification significantly but also increases the efficiency and
signal-background ratio. Besides, boosted neural network can avoid the
disadvantage aspects of single neural network design. The boosted neural
network is also applied to the classification of quark- and gluon- jet samples
from Monte Carlo \EE collisions, where the two samples show significant
overlapping. The performance of boosting technique for the two different
boundary cases -- with and without overlapping is discussed.
