Due to the differentiation of landslides in Kraków city area, an artificial neural network method (multilayer perceptron) was used to determine the landslide susceptibility (LS). The calculations were performed in the r.landslide module. The network learning was carried out on the basis of 8 thematic layers (slopes, slope exposure, absolute height, relative height, convergence index, surface lithology, sub-Quaternary lithology, distance from tectonic discontinuities). For modelling, 434 points representing landslides and the same number of pointsoflocationswithoutlandslideswereused.Amongthesetofpoints,70%wasallocatedtothetrainingphase, 15%tothevalidationphase,and15%tothephase.Inordertoassessthenetworkperformance,basedontheresults of the test phase, a confusion matrix was made. Approximately 22% of the city’s area is susceptible to landslide occurrence(LS>0.05).Itoverlapexistinglandslidesandcoverareaswheretheyhavenotoccurredyet.Thegreatestnumberofareas susceptibletolandslideoccurrenceislocatedindistrictsX(54%ofthedistrictarea)andVII(47%).Therearealsothemostsusceptible areas (LS > 0.95). The sensitivity analysis implemented in the module showed that among the thematic layers used for modelling the slopes, convergence index, distance from tectonic discontinuities and sub-Quaternary lithology have the greatest impact on the landslide susceptibility.