  Background showers triggered by hadrons represent over 99.9% of all particles
arriving at ground-based gamma-ray observatories. An important stage in the
data analysis of these observatories, therefore, is the removal of
hadron-triggered showers. Currently, the High-Altitude Water Cherenkov (HAWC)
gamma-ray observatory employs an algorithm based on a single cut in two
variables, unlike other ground-based gamma-ray observatories (e.g. H.E.S.S.,
VERITAS), which employ a large number of variables to separate the primary
particles. In this work, we explore machine learning techniques (Boosted
Decision Trees and Neural Networks) to identify the primary particles detected
by HAWC. Our new gamma/hadron separation techniques were tested on data from
the Crab nebula, the standard reference in Very High Energy astronomy, showing
an improvement compared to the standard HAWC background rejection method.
