  This paper describes applications of two neural networks to improve drift
chamber position measurements. One network calculates a data-driven estimate of
the drift cell time-to-distance relationship that is conventionally estimated
by a numerical calculation based on the anode and cathode wire geometry, wire
potentials, and gas properties. The second network additionally uses the full
digital waveform of the signal in the drift chamber, hence accessing
information on the full ensemble of ionization clusters. This network uses more
information than the conventional position estimate that relies exclusively on
the arrival time of the first drift electron. In principle, this technique
improves resolution even when multiple ionization clusters cannot be separated,
in contrast with a cluster-counting technique. The performance of both networks
when applied to MEG II drift chamber data is reported and compared to that of a
conventional approach.
