Matching estimators based on the propensity score are widely used in the field of treatment effect evaluation and a viable technique also for missing data imputation. This paper describes an implementation of the technique in SAS®, a statistical software where only limited implementations of it are currently available. The user can choose among the most common variants of the matching algorithms (nearest neighbour-, stratification- and kernel matching), and the main pre- and post estimation analyses proposed in the literature (reduction of standardised bias, Sianesi test, Ichino-Becker test on the balancing hypotheses, Lechner bounds). To these, two additional diagnostics are proposed in order to better monitor some aspects of the matching process. A validation of the procedure is made using the available STATA tools as a benchmark, with both artificial data and data already used in the literature.