Manual cleaning of pig production buildings based on high-pressure water cleaners is unappealing to workers, because it is tedious and health threatening. To replace manual cleaning, a few cleaning robots have been commercialised. With no cleanliness sensor available, the operation of these robots is to follow a cleaning procedure initially defined by the operator. Experience shows that the performance of such robots is poor regarding effectiveness of cleaning and utilisation of water. The development of an intelligent cleanliness sensor for robotic cleaning is thus crucial in order to optimise the cleaning process and to minimise the amount of water and electricity consumed. This research is aimed at utilising a spectral imaging method for cleanliness detection. Consequently, information on the reflectance of building materials and contamination in different spectral ranges is important. In this study, the optical properties of different types of surfaces to be cleaned and the dirt found in finishing pig units were investigated in the visual and the near infrared (VIS-NIR) optical range. Four types of commonly used materials in pig buildings, i.e. concrete, plastic, wood and steel were applied in the investigation. Reflectance data were sampled under controlled lighting conditions using a spectrometer communicating with a portable computer. The measurements were performed in a laboratory with materials used in a pig house for 4-5 weeks. The spectral data were collected for the surfaces before, during and after high-pressure water cleaning. The spectral signatures of the surface materials and dirt attached to the surfaces showed that it is possible to make discrimination and hence to classify areas that are visually clean. When spectral bands 450, 600, 700 and 800 nm are chosen, there are at least two spectral bands for each type of the materials, in which the spectral signals can be used for discrimination of dirty and clean condition of the surfaces.