  The reconstruction of the invariant mass of $\tau$ lepton pairs is important
for analyses containing Higgs and Z bosons decaying to $\tau^{+}\tau^{-}$, but
highly challenging due to the neutrinos from the $\tau$ lepton decays, which
cannot be measured in the detector. In this paper, we demonstrate how
artificial neural networks can be used to reconstruct the mass of a di-$\tau$
system and compare this procedure to an algorithm used by the CMS Collaboration
for this purpose. We find that the neural network output shows a smaller bias
and better resolution of the di-$\tau$ mass reconstruction and an improved
discrimination between a Higgs boson signal and the Drell-Yan background with a
much shorter computation time.
