Refractory products are essential for high-temperature industrial processes, from steel and cement production to waste incineration and many more. Harsh environmental conditions in these applications result in the frequent renewal of refractory lining. As a consequence of this maintenance, spent refractories are generated which possess high circular raw material val-ue. Current recycling efforts include manual sorting based on visual criteria, with size limita-tions due to economic factors. To this date no holistic automated recycling process has been developed capable of sorting the sheer variety of different refractory materials, independent of their grain size. The EU-funded project ReSoURCE aims to develop such an automated sorting solution based on a sensor setup consisting of HSI (hyperspectral imaging) and LIBS (laser induced breakdown spectroscopy) combined with artificial intelligence. For this pur-pose, a database is created using sensor measurements and analysis results from typical primary raw and breakout materials of varying types and sizes. The presented study evalu-ates the suitability of each sensor for refractory sorting purposes. First tests showing promis-ing results for the differentiation of testing materials. Future work will involve the expansion of the database and the further definition of chemically and mineralogically differentiable sort-ing classes.