  HAWC is a ground-based observatory consisting of 300 water Cherenkov
detectors, which observes the extensive air showers induced by cosmic rays from
some TeV to a few PeV and, in particular, gamma rays from 300 GeV to more than
100 TeV. One of the crucial features required for a detector of extensive air
showers is the estimation of the primary energy of the events to study the
spectra of cosmic and gamma rays. For HAWC there are currently two gamma-ray
energy estimators: one relies on a ground density parameter, while the other
utilizes an artificial neural network. For the cosmic ray energy estimation,
there is only one estimator based on maximum likelihood procedures and
measurements of the lateral charge distribution of the events. It is worthwhile
to update the cosmic-ray energy estimator due to recent improvements of the
extensive air shower offline-reconstruction techniques in HAWC. Therefore, we
implemented an artificial neural network to reconstruct the primary energy of
hadronic events trained with several observables that characterize the air
showers. We trained several models and evaluated their performance against the
existing cosmic ray energy estimator. In this work, we present the features and
performance of these models.
