Basic soil properties have long been used to predict unsaturated soil hydraulic properties with pedotransfer function (PTFs). Implementation of such PTFs is usually not feasible for catchment-scale studies because of the experimental effort that would be required. On the other hand, topographical attributes are often readily available. This study therefore examines how well PTFs perform that use both basic soil properties and topographical attributes for a hillslope in Basilicata, Italy. Basic soil properties and hydraulic data were determined on soil samples taken at 50-m intervals along a 5-km hillslope transect. Topographical attributes were determined from a digital elevation model. Spearman coefficients showed that elevation (z) was positively correlated with organic carbon (OC) and silt contents (0.62 and 0.59, respectively) and negatively with bulk density (ρb) and sand fraction (-0.34 and -0.37). Retention parameters were somewhat correlated with topographical attributes z, slope (¿), aspect (cosφ), and potential solar radiation. Water contents were correlated most strongly with elevation (coefficient between 0.38 and 0.48) and aspect during wet conditions. Artificial neural networks (ANNs) were developed for 21 different sets of predictors to estimate retention parameters, saturated hydraulic conductivity (Ks), and water contents at capillary heads h = 50 cm and 12 bar (103 cm). The prediction of retention parameters could be improved with 10% by including topography (RMSE = 0.0327 cm3 cm-3) using textural fractions, ρb, OC, z, and ¿ as predictors. Furthermore, OC became a better predictor when the PTF also used z as predictor. The water content at h = 50 cm could be predicted 26% more accurately (RMSE = 0.0231 cm3cm-3) using texture, ρb, OC, z, ¿, and potential solar radiation as input. Predictions of ANNs with and without topographical attributes were most accurate in the wet range (0 < h < 250 cm). Semivariograms of the hydraulic parameters and their residuals showed that the ANNs could explain part of the (spatial) variability. The results of this study confirm the utility of topographical attributes such as z, ¿, cosφ, and potential solar radiation as predictors for PTFs when basic soil properties are available. A next step would be the use of topographical attributes when no or limited other predictors are available.