We present the results of the implementation of a deep learning system that estimates the effective temperature and surface gravity of O-type stars. The proposed system was trained with a database of 5,557 synthetic spectra calculated with the CMFGEN stellar atmosphere code and covers stars with Teff from ∼20000 K to ∼58000 K, log(L/L⊙) from 4.3 to 6.3 dex, log g from 2.4 to 4.2 dex, and mass from 9 to 120 M⊙
The validation of the system was performed by processing a sample of twenty O-type stars taken from the IACOB database, and a subgroup of eleven stars of those twenty taken from The Galactic O-Star Spectroscopic Catalog (GOSC) with lower resolution.