The tactile perception of material properties is a difficult task, but also of great importance for the skillful manipulation of objects in fields such as robotics, virtual reality and augmented reality. Given the diversity of material properties, integrated tactile perception systems require efficient extraction and classification of features from signals collected by tactile sensors. This paper focuses on the development and validation of an automatic learning system for the classification of tactile data in form of vibrotactile (accelerometer) and audio (microphone) data for texture recognition. The tests carried out have shown that among the extracted features, the combination of key compnents obtains the best results. These include the standard deviation, the mean, the absolute median of the deviation, and the energy that characterizes the power of the signal, a measure which reflects the perceptual properties of the human system associated with each sensory modality. Moreover, the Fourier characteristics extracted from the vibro-tactile and audio signals contribute to the quality of the perception. In order to reduce the dimensionality of the tactile dataset and identify the most compact models, we apply principal component analysis and a selection process of the features based on their importance. Several machine learning models including Naïve Bayes classification, the K-nearest neighbors algorithm, decision trees, random forests, support vector machines, logistic regression, neural networks, XGBoost (Extreme Gradient Boosting) and XGBRF (a combination of random forests as a framework and the XGBoost algorithm) are compared in an attempt to identify the best compromise between the number of features, the classification performance and the computation time. Moreover, we demonstrate that the choice of the sampling length from the tactile signals is an important aspect that can have a significant impact on classification accuracy.