  We investigate a neural network-based hypothesis test to distinguish
different $W'$ and charged scalar resonances through the
$\ell+\require{cancel}\cancel{E}_T$ channel at hadron colliders. This is
traditionally challenging due to a four-fold ambiguity at proton-proton
colliders, such as the Large Hadron Collider. Of the neural network approaches
we studied, we find a multi-class classifier based on a fully-connected neural
network trained upon 2D histograms made from kinematic variables of the final
state $\ell$ to be the most powerful. Furthermore, by considering the 1-jet
processes, we demonstrate that one can generalize to multiple $2D$ histograms
to represent different variable pairs. Finally, as a comparison to traditional
approaches, we compare our method with Bayesian hypothesis testing and discuss
the pros and cons of each approach. The neural network scheme presented in this
paper is a powerful tool that can help probe the properties of charged
resonances.
