To assess the wolf-habitat relationship on the home range scale (i.e., third order selection), we developed a resource selection probability function (RSPF) through a multiple logistic regression model based on the winter travel routes of a wolf pack in the northern Apennines, Italy (1991–95). Both travel routes (240 km) and habitat variables were mapped at 1:10 000 scale, digitised as Geographical Information System (GIS) layers, and overlaid with a 100×100 m pixel grid to census all used and unused resource units. Out of 15 covariates, the full model included 10 variables and 4 interaction terms. According to the model, travel routes by wolves were not randomly located within the home range but were clearly associated with selected bio-physical factors, including human-related habitat modifications (i.e., roads), which appeared to affect the wolves' resource selection and, ultimately, habitat quality. Using a jackknife procedure, the model correctly classified 73.1% of used resource units and 63.2% of unused resource units. A Monte Carlo test showed a non-significant effect on the model coefficients of 4 increasing sub-sampling levels of used resource units, suggesting that autocorrelation of snow-tracking data exerted little influence on the point estimates of the coefficients. However, given the increased standard error at higher sub-sampling levels, autocorrelation might have caused an underestimation of the theoretical variance. Although wolves are generally considered habitat generalists, this study shows that patterns of habitat selection are disclosed at finer scales of analysis. In this perspective, resource selection probability functions at finer scales offer different and complementary insight with respect to regional landscape applications, and provide a useful management tool for assessment of habitat quality at the local scale.