  Centrality, as a geometrical property of the collision, is crucial for the
physical interpretation of nucleus-nucleus and proton-nucleus experimental
data. However, it cannot be directly accessed in event-by-event data analysis.
Common methods for centrality estimation in A-A and p-A collisions usually rely
on a single detector (either on the signal in zero-degree calorimeters or on
the multiplicity in some semi-central rapidity range). In the present work, we
made an attempt to develop an approach for centrality determination that is
based on machine-learning techniques and utilizes information from several
detector subsystems simultaneously. Different event classifiers are suggested
and evaluated for their selectivity power in terms of the number of
nucleons-participants and the impact parameter of the collision. Finer
centrality resolution may allow to reduce impact from so-called volume
fluctuations on physical observables being studied in heavy-ion experiments
like ALICE at the LHC and fixed target experiment NA61/SHINE on SPS.
