  We propose a method to extract high-energy hadronic interaction properties
from the distributions of two of the main observables of proton extensive air
showers: the depth of maximum shower development, $X_\mathrm{max}$, and the
number of muons at the ground, $N_\mu$. We determine relevant parameters of the
first and subsequent interactions of the cascade and analyse how they impact on
these observables. By training a universal neural network, we demonstrate that
we can recover the most relevant parameters (fraction of energy going to the
hadronic channel in the first interaction, first interaction multiplicity and
effective inelasticity) for different hadronic interaction models using only
the observables $X_\mathrm{max}$ and $N_\mu$.
