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In this paper, we present an application of machine learning to distinguish between different materials based on their surface texture. Such a system can be used for the estimation of surface friction during manipulation tasks; quality assurance in the textile, cosmetics, and harvesting industries; and other applications requiring tactile sensing. Several machine learning algorithms, such as naive Bayes, decision trees, and naive Bayes trees, have been trained to distinguish textures sensed by a biologically inspired artificial finger. The finger has randomly distributed strain gauges and polyvinylidene fluoride (PVDF) films embedded in silicone. Different textures induce different intensities of vibrations in the silicone. Consequently, textures can be distinguished by the presence of different frequencies in the signal. The data from the finger are preprocessed, and the Fourier coefficients of the sensor outputs are used to train classifiers. We show that the classifiers generalize well for unseen datasets with performance exceeding previously reported algorithms. Our classifiers can distinguish between different materials, such as carpet, flooring vinyls, tiles, sponge, wood, and polyvinyl-chloride (PVC) woven mesh with an accuracy of on unseen test data.