  The H.E.S.S. experiment entered its phase II with the addition of a new,
large telescope named CT 5 that was added to the centre of the existing array
of four smaller telescopes. The new telescope is able to detect fainter air
showers due to its larger mirror area, thereby lowering the energy threshold of
the array from a few hundred GeV down to $\mathcal{O}(50\,\textrm{GeV})$. Due
to the power-law decrease of typical {\gamma}-ray and cosmic-ray spectra of
astrophysical sources a majority of detected air showers are of low energies,
thus being detected by CT 5 only, which motivates the need for a reconstruction
algorithm based on information from CT 5 alone. By exploiting such monoscopic
events the H.E.S.S. experiment in phase II becomes sensitive in an energy range
not covered by H.E.S.S. I and in which the Fermi LAT runs out of statistics.
Furthermore the chance of detecting transient phenomena like {\gamma}-ray
bursts is increased significantly due to the large effective area of CT 5 at
low energies.
  In this contribution a newly developed reconstruction algorithm for
monoscopic events based on neural networks is presented. This algorithm uses
multilayer perceptrons to reconstruct the direction and energy of the particle
initiating the air shower and also to discriminate between gamma rays and
hadrons. The performance of this algorithm is evaluated and compared to other
existing reconstruction algorithms. Furthermore results of first applications
of the algorithm to measured data are shown.
