  The Fermi Large Area Telescope (LAT) has detected more than 5000 gamma-ray
sources in its first 8 years of operation. More than 3000 of them are blazars.
About 60 per cent of the Fermi-LAT blazars are classified as BL Lacertae
objects (BL Lacs) or Flat Spectrum Radio Quasars (FSRQs), while the rest remain
of uncertain type. The goal of this study was to classify those blazars of
uncertain type, using a supervised machine learning method based on an
artificial neural network, by comparing their properties to those of known
gamma-ray sources. Probabilities for each of 1329 uncertain blazars to be a BL
Lac or FSRQ are obtained. Using 90 per cent precision metric, 801 can be
classified as BL Lacs and 406 as FSRQs while 122 still remain unclassified.
This approach is of interest because it gives a fast preliminary classification
of uncertain blazars. We also explored how different selections of training and
testing samples affect the classification and discuss the meaning of network
outputs.
