Yersinia enterocolitica and other Yersinia species, such as Y. pseudotuberculosis, Y. bercovieri, and Y. intermedia, were differentiated using Fourier transform infrared spectroscopy (FT-IR) combined with artificial neural network analysis. A set of well defined Yersinia strains from Switzerland and Germany was used to create a method for FT-IR-based differentiation of Yersinia isolates at the species level. The isolates of Y. enterocolitica were also differentiated by FT-IR into the main biotypes (biotypes 1A, 2, and 4) and serotypes (serotypes O:3, O:5, O:9, and “non-O:3, O:5, and O:9”). For external validation of the constructed methods, independently obtained isolates of different Yersinia species were used. A total of 79.9% of Y. enterocolitica sensu stricto isolates were identified correctly at the species level. The FT-IR analysis allowed the separation of all Y. bercovieri, Y. intermedia, and Y. rohdei strains from Y. enterocolitica, which could not be differentiated by the API 20E test system. The probability for correct biotype identification of Y. enterocolitica isolates was 98.3% (41 externally validated strains). For correct serotype identification, the probability was 92.5% (42 externally validated strains). In addition, the presence or absence of the ail gene, one of the main pathogenicity markers, was demonstrated using FT-IR. The probability for correct identification of isolates concerning the ail gene was 98.5% (51 externally validated strains). This indicates that it is possible to obtain information about genus, species, and in the case of Y. enterocolitica also subspecies type with a single measurement. Furthermore, this is the first example of the identification of specific pathogenicity using FT-IR.