Many of observed phenomena associated to physics experiments do not have a model that allow a good description. If some effects depend on known parameters in the way which cannot be well described, neural network can be a useful tool to solve occurring problems. In this paper neural network is applied to eliminate the false coincidences (cross-talk) in two-neutron correlation function analysis.

PACS numbers: 25.75.Gz, 87.80.Xa