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Description: Materials exhibit an extraordinary range of visual appearances. Characterising and quantifying appearance is important not only for basic research on perceptual mechanisms, but also for computer graphics and a wide range of industrial applications. While methods exist for capturing and representing the optical properties of materials and how they vary across surfaces (Haindl & Filip., 2013), the representations are typically very high-dimensional, and how these representations relate to subjective perceptual impressions of material appearance remains poorly understood. Here, we used a data-driven approach to characterising the perceived appearance characteristics of 30 samples of wood veneer using a ‘visual fingerprint’ that describes each sample as a multidimensional feature vector, with each dimension capturing a different aspect of the appearance. Fifty-six crowd-sourced participants viewed triplets of movies depicting different wood samples as the sample rotated. Their task was to report which of the two match samples was subjectively most similar to the test sample. In another online experiment 45 participants rated ten wood-related appearance characteristics for each of the samples. The results reveal a consistent embedding of the samples across both experiments and a set of 9 perceptual dimensions capturing aspects including the roughness, directionality and spatial scale of the surface patterns. We also showed that a weighted linear combination of eleven image statistics, inspired by the rating characteristics, predicts perceptual dimensions well.