Evaluations of vocational education and training (VET) programs play a key role in informing training policy in Australia and elsewhere. Increasingly, such evaluations use observational data from surveys or administrative collections to assess the effectiveness of VET programs and interventions. The difficulty associated with using observational data is that they are inherently prone to selection bias, which results from individuals self-selecting into a given VET program based on differences in background characteristics or other external factors. The effects of the VET program on outcomes of interest are thus confounded with the effects of pre-existing systematic differences between program participants and non-participants. Propensity score matching (PSM) can mitigate selection bias in evaluation studies with observational data by statistically balancing program participants and non-participants post hoc on observed background characteristics. This article seeks to offer a general introduction to PSM and to provide interested VET researchers with an initial stepping stone for using the method in their own work. (Contains 2 figures, 1 table and 2 footnotes.)