EN
Revisions and redescriptions of species and higher taxa have been known in parasitology since the first description of a parasite. Usually, they are based on standard morphometric methods or more modern genetic analysis. The former are not always sufficiently reliable, while the latter often require expensive equipment, pre-defined genetic markers, and appropriately prepared research material. They may be replaced by multivariate statistical methods, in particular discriminant analysis and cluster analysis, and Kohonen artificial neural networks included in data mining. This paper presents the examples of specific applications of these methods for the verification of the affinity of nematodes. The discriminant analysis showed that it was possible to statistically significantly discriminate individual nematode species, both for males and females, based on morphometric variables. This confirmed the previously assumed division of the species complex Amidostomum acutum into three distinct species. Similarly, hierarchical cluster analysis, used for the determination of coherent groups of nematode parasites, allowed the identification of relatively homogeneous clusters of nematode species depending on their circle of hosts, and groups of hosts.