  In its first four years of operation, the Fermi Large Area Telescope (LAT)
detected 3033 $\gamma$-ray emitting sources. In the Fermi-LAT Third Source
Catalogue (3FGL) about 50% of the sources have no clear association with a
likely $\gamma$-ray emitter. We use an artificial neural network algorithm
aimed at distinguishing BL Lacs from FSRQs to investigate the source subclass
of 559 3FGL unassociated sources characterised by $\gamma$-ray properties very
similar to those of Active Galactic Nuclei. Based on our method, we can
classify 271 objects as BL Lac candidates, 185 as FSRQ candidates, leaving only
103 without a clear classification. we suggest a new zoo for $\gamma$-ray
objects, where the percentage of sources of uncertain type drops from 52% to
less than 10%. The result of this study opens up new considerations on the
population of the $\gamma$-ray sky, and it will facilitate the planning of
significant samples for rigorous analyses and multiwavelength observational
campaigns.
