  This paper evaluates the performance of boosted decision trees for tagging
b-jets. It is shown, using a Monte Carlo simulation of $WH \to l\nu q\bar{q}$
events that boosted decision trees outperform feed-forward neural networks. The
results show that for a b-tagging efficiency of 60% the light jet rejection
given by boosted decision trees is about 35% higher than that given by neural
networks.
