  Imaging atmospheric Cherenkov telescopes record an enormous number of
cosmic-ray background events. Suppressing these background events while
retaining $\gamma$-rays is key to achieving good sensitivity to faint
$\gamma$-ray sources. The differentiation between signal and background events
can be accomplished using machine learning algorithms, which are already used
in various fields of physics. Multivariate analyses combine several variables
into a single variable that indicates the degree to which an event is
$\gamma$-ray-like or cosmic-ray-like. In this paper we will focus on the use of
boosted decision trees for $\gamma$/hadron separation. We apply the method to
data from the Very Energetic Radiation Imaging Telescope Array System
(VERITAS), and demonstrate an improved sensitivity compared to the VERITAS
standard analysis.
