Discrimination of bacteria was investigated using pyrolysis-gas chromatography-differential mobility spectrometry (Py-GC-DMS). Three strains belonging to the genus Bacillus were investigated and these included two strains of Bacillus subtilis and a single Bacillus megaterium. These were chosen so as to evaluate the possibility of bacterial strain discrimination using Py-GC-DMS. The instrument was constructed in-house and the long-term reproducibility of the instrument was evaluated over a period of 60 days using a Scotch whisky quality control. To assess the reproducibility further each bacterium was cultured six times and each culture was analysed in replicate to give three analytical replicates. The DMS data were generated in both positive and negative modes, and the data in each mode were analysed independently of each other. The Py-GC-DMS data were pre-processed via correlation optimised warping (COW) and asymmetric least square (ALS) to align the DMS chromatograms and to remove any unavoidable baseline shifts, prior to normalisation. Processed chromatograms were analysed using principal component analysis (PCA) followed by supervised learning methodology using partial least squares for discriminant analysis (PLS-DA). It was found that the separations between B. subtilis and B. megaterium can be readily observed by PCA; however, strain discrimination within the two B. subtilis was only possible using supervised learning. As multiple biological replicates were analysed an exhaustive splitting of the training and test sets was undertaken and this allowed correct classification rates (CCRs) to be assessed for the 3375 test sets. It was found that with PLS-DA the negative ion mode DMS data were more discriminatory than the positive mode data.