  A pulse-shape discrimination method based on artificial neural networks was
applied to pulses simulated for different background, signal and signal-like
interactions inside a germanium detector. The simulated pulses were used to
investigate variations of efficiencies as a function of used training set. It
is verified that neural networks are well-suited to identify background pulses
in true-coaxial high-purity germanium detectors. The systematic uncertainty on
the signal recognition efficiency derived using signal-like evaluation samples
from calibration measurements is estimated to be 5\%. This uncertainty is due
to differences between signal and calibration samples.
