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Near infrared (NIR) hyperspectral imaging and multivariate data analysis was evaluated for its potential to detect and classify Listeria species. Three Listeria species, namely L. monocytogenes (ATCC 23074), L. innocua (ATCC 33090) and L. ivanovii (ATCC 19119) were grown for single colonies on Brain Heart Infusion agar and imaged in the NIR range of 950–2500 nm. Principal component analysis (PCA) was used for data exploration and to establish pattern recognition. Images were pre-processed with standard normal variate correction and the Savitzky-Golay smoothing technique (third order polynomial with 15 points). Two approaches to data analysis, that is object-wise and pixel-wise analysis, were investigated for discriminant analysis. The PCA score plot showed slight separation between the three groups with L. monocytogenes and L. ivanovii grouping close together. It was possible to visualise separation along PC3 (5.64% sum of squares (SS)) and PC4 (3.44% SS). Based on the loadings, differences in bacteria were attributed to teichoic acids, protein, and carbohydrate composition in the bacterial cell wall within the wavelength range 1000–1900 nm. Using extracted spectral data from the hypercubes, partial least squares discriminant analysis was employed for further classification. Classification accuracies above 90% were achieved for L. monocytogenes, L. innocua and L. ivanovii . This was true for data analysed using both pixel-wise analysis and object-wise analysis. The results demonstrated that hyperspectral imaging has notable potential to classify bacteria within the Listeria genus. Nonetheless, in order to improve model efficiency, model optimisation and incorporation of more bacterial strains need to be investigated in further research. Keywords Agar , food safety , species , multivariate data analysis , near infrared hyperspectral imaging , partial least discriminant analysis , single colonies