  The Fermi Large Area Telescope (LAT) is currently the most important facility
for investigating the GeV $\gamma$-ray sky. With Fermi LAT more than three
thousand $\gamma$-ray sources have been discovered so far. 1144 ($\sim40\%$) of
the sources are active galaxies of the blazar class, and 573 ($\sim20\%$) are
listed as Blazar Candidate of Uncertain type (BCU), or sources without a
conclusive classification. We use the Empirical Cumulative Distribution
Functions (ECDF) and the Artificial Neural Networks (ANN) for a fast method of
screening and classification for BCUs based on data collected at $\gamma$-ray
energies only, when rigorous multiwavelength analysis is not available. Based
on our method, we classify 342 BCUs as BL Lacs and 154 as FSRQs, while 77
objects remain uncertain. Moreover, radio analysis and direct observations in
ground-based optical observatories are used as counterparts to the statistical
classifications to validate the method. This approach is of interest because of
the increasing number of unclassified sources in Fermi catalogs and because
blazars and in particular their subclass High Synchrotron Peak (HSP) objects
are the main targets of atmospheric Cherenkov telescopes.
