The fourth industrial revolution makes extensive use of IoT, AI, and smart sensors for improved automation, safety, production, and prognostics, and health management. In this paper, we address corrective maintenance based on fault recognition relying on sounds produced by machine components. Different spectral features are extracted from industrial sounds and are used as input of supervised learning algorithms for classification between normal and abnormal operations. Experiments using the MIMII (Malfunctioning Industrial Machine Investigation and Inspection) dataset, which contains sound samples produced by pump, slide rail, valve, and fan components, reveals promising results based on the f1-score. We also evaluate the impact of the different spectral features considered, confirming their incremental impact. Finally, we compare our proposal with a baseline alternative from the literature, which employs unsupervised learning and Mel-spectrogram conversion. Our approach improves the AUC (Area Under the Curve) metric by up to 39.5% compared with the baseline approach.