  We discuss a method that employs a multilayer perceptron to detect deviations
from a reference model in large multivariate datasets. Our data analysis
strategy does not rely on any prior assumption on the nature of the deviation.
It is designed to be sensitive to small discrepancies that arise in datasets
dominated by the reference model. The main conceptual building blocks were
introduced in Ref. [1]. Here we make decisive progress in the algorithm
implementation and we demonstrate its applicability to problems in high energy
physics. We show that the method is sensitive to putative new physics signals
in di-muon final states at the LHC. We also compare our performances on toy
problems with the ones of alternative methods proposed in the literature.
