A: The Indian Scintillator Matrix for Reactor Anti-Neutrino detection -ISMRAN experiment aims to detect electron anti-neutrinos (ν e ) emitted from a reactor via inverse beta decay reaction (IBD). The setup, consisting of 1 ton segmented Gadolinium foil wrapped plastic scintillator array, is planned for remote reactor monitoring and sterile neutrino search. The detection of prompt positron and delayed neutron from IBD will provide the signature of ν e event in ISMRAN. The number of segments with energy deposit (N bars ) and sum total of these deposited energies are used as discriminants for identifying prompt positron event and delayed neutron capture event. However, a simple cut based selection of above variables leads to a low ν e signal detection efficiency due to overlapping region of N bars and sum energy for the prompt and delayed events. Multivariate analysis (MVA) tools, employing variables suitably tuned for discrimination, can be useful in such scenarios. In this work we report the results from artificial neural network classifierthe multilayer perceptron (MLP), particularly the Bayesian extension -MLPBNN, to achieve better signal detection efficiencies with reasonable background rejection. The neural network response is used to distinguish prompt positron events from delayed neutron capture events on Hydrogen, Gadolinium nucleus, and from a typical reactor γ-ray background. A prompt signal efficiency of ∼91% with a reasonable background rejection of ∼73% is achievable with the MLPBNN classifier for the ISMRAN experiment.