Among safeguards verification activities, spent nuclear fuel is placed under inspections for the detection of possible diversions of the fissile material present, being of particular concern from the non-proliferation point of view. Several Non-Destructive Assay are utilized, or under investigation, for the verification of spent nuclear fuel. Among the latter, the Partial Defect Tester (PDET) is the one considered in this work. In this framework, some machine learning methods have been implemented in this thesis project, in order to detect the replacement of fuel pins in spent nuclear fuel assemblies. The input features of the models were based on different combinations of types and locations of detector responses, whose values were provided by a dataset of Monte Carlo simulations, based on the PDET prototype.