  We present an application of an artificial neural network methodology to a
modern wide-field sky survey Pan-STARRS1 in order to build a high-quality
sample of disk galaxies visible in edge-on orientation. Such galaxies play an
important role in the study of the vertical distribution of stars, gas and
dust, which is usually not available to study in other galaxies outside the
Milky Way. We give a detailed description of the network architecture and the
learning process. The method demonstrates good effectiveness with detection
rate about 97\% and it works equally well for galaxies over a wide range of
brightnesses and sizes, which resulted in a creation of a catalogue of edge-on
galaxies with $10^5$ of objects. The catalogue is published on-line with an
open access.
