We present a framework for the acquisition and parametrization of object material properties. The introduced acquisition device, denoted as Texplorer2, is able to extract surface material properties while a human operator is performing exploratory procedures. Using the Texplorer2, we scanned 184 material classes which we labeled according to biological, chemical, and geological naming conventions. Based on these real material recordings, we introduce a novel set of mathematical features which align with corresponding material properties defined in perceptual studies from related work and classify the materials using common machine learning techniques. Validation results of the proposed multi-modal features lead to an overall classification accuracy of 90.2% ± 1.2% and an F[Formula: see text] score of 0.90 ± 0.01 using the random forest classifier. For the sake of comparison, a deep neural network is trained and tested on images of the material surfaces; it outperforms (90.7% ± 1.0%) the hand-crafted feature-based approach yet leads to more critical misclassifications in terms of the proposed taxonomy.