The real world is abundant with a diverse array of materials, each possessing unique surface appearances that play a crucial role in our daily perception and understanding of their properties. Despite advancements in technology enabling the realistic reproduction of material appearances for visualization and quality control, the interoperability of material property information across various measurement representations and software platforms remains a complex challenge. A key to overcoming this challenge lies in the automatic identification of materials’ perceptual features, enabling intuitive differentiation of properties stored in disparate material data formats. This paper introduces a novel approach to material identification by encoding perceptual features obtained from dynamic visual stimuli. We conducted a psychophysical experiment to identify and validate 16 particularly significant perceptual attributes across 347 materials. Subsequently, we gathered attribute ratings from 20–24 participants for each material, creating a ‘material signature’ that encodes the perceptual properties of each material.