  With the advent of large-scale photometric surveys of the sky, modern science
witnesses the dawn of big data astronomy, where automatic handling and
discovery are paramount. In this context, classification tasks are among the
key capabilities a data reduction pipeline must possess in order to compile
reliable datasets, to accomplish data processing with an efficiency level
impossible to achieve by means of detailed processing and human intervention.
The VISTA Variables of the V\'ia L\'actea Survey, in the southern part of the
Galactic disc, comprises multi-epoch photometric data necessary for the
potential discovery of variable objects, including eclipsing binary systems
(EBs). In this study we use a recently published catalogue of one hundred EBs,
classified by fine-tuning theoretical models according to contact, detached or
semi-detached classes belonging to the tile d040 of the VVV. We describe the
method implemented to obtain a supervised machine learning model, capable of
classifying EBs using information extracted from the light curves of variable
object candidates in the phase space from tile d078. We also discuss the
efficiency of the models, the relative importance of the features and the
future prospects to construct an extensive database of EBs in the VVV survey.
