This paper presents a feasibility study of surface geometry (SG) evaluation and material classification (MC) for robotic spraying. We propose two complementary approaches using point clouds and intensity data provided by a state-of-the-art industrial time-of-flight (ToF) depth camera. The SG evaluation is based on geometric feature computation within local neighbourhoods, which are then used within a supervised classification. The results of this approach are SG classes according to the level of geometric variability of the surface, displayed as SG maps. For MC, active reflectance estimation is investigated and exploited to derive features related to the reflectance and diffusive properties of each material for classification. The result of both approaches can be prospectively used as feedback in digital fabrication for in-line adaptation of the process to improve control of relevant geometrical and material properties.