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Material recognition is an essential problem in the industrial automation community. In this study, we develop a machine learning method to identify the wood material. We extract various feature descriptors for the sound signal and perform a comprehensive comparison with the common classifier. The experimental validations achieve the best feature combination. Besides, we developed a practical stylus to collect sound signal from the wooden samples and present promising preliminary results.