  The paper explores the feasibility of using machine learning techniques, in
particular neural networks, for classification of the experimental data from
the joint $^\text{nat}$C(n,p) and $^\text{nat}$C(n,d) reaction cross section
measurement from the neutron time of flight facility n_TOF at CERN. Each
relevant $\Delta E$-$E$ pair of strips from two segmented silicon telescopes is
treated separately and afforded its own dedicated neural network. An important
part of the procedure is a careful preparation of training datasets, based on
the raw data from Geant4 simulations. Instead of using these raw data for the
training of neural networks, we divide a relevant 3-parameter space into
discrete voxels, classify each voxel according to a particle/reaction type and
submit these voxels to a training procedure. The classification capabilities of
the structurally optimized and trained neural networks are found to be superior
to those of the manually selected cuts.
