Diverse techniques were applied to effect the identification and classification of isolated clostridial strains. Nevertheless, the correct identification of clostridial strains remains a laborious, time-consuming task which entails a not inconsiderable degree of expertise. In addition to this, traditional methods based on the metabolic properties of the bacteria require rigorously standardized media and growth conditions to assure the attainment of reproducible results. Although DNA-based methods, like the PCR of a species specific gene, are known to yield precise and reproducible results, their degree of effectivity is circumscribed by the fact that even the incidence of a toxin encoding gene is not necessarily linked to nor consequently indicative of the presence of an infectious disease. Moreover, most of these methods postulate an initial assumption concerning the expected bacterial species involved before the choice of PCR primer for use can be made. Consequently, the scope of these methods is restricted to that of targeted analyses. The 16S rDNA sequencing which is assumed to be the gold standard for bacterial classification having the unequivocal advantage of being capable of determining even uncultivable bacteria is nonetheless a time-consuming and costly technique. In the present study we describe the utilization of matrix-assisted laser desorption and ionization-time-of-flight mass spectrometry (MALDI-TOF MS) for whole cell fingerprinting in combination with a dedicated bioinformatic software tool to distinguish between various clostridial species. Total 64 clostridial strains of 31 different species each displayed a mass spectrum unique to the strain involved, to the effect that it was also possible not only to differentiate between the strains examined, but also to establish to which species the individual strains belonged to. Starting with a single colony it was possible to correctly identify a Clostridium species within minutes. It was even possible to identify species which are normally difficult to differentiate by traditional methods, such as C. chauvoei and C. septicum. With the results obtained we were able to assemble a dendrogram of the Clostridium species which showed considerable similarities to dendrograms based upon 16S rDNA sequencing data. To conclude, our findings indicate that, inasmuch as the MALDI-TOF MS technology employed is based on a high-quality reference database, it may serve as an effective tool for the swift and reliable identification and classification of Clostridia.