Sensor integration for in situ monitoring during additive manufacturing promises to enhance control over the process and assures quality in the fabricated workpieces. Acoustic emissions from the process zone of the laser powder-bed fusion process carry information about the events and failure modes of the printed workpiece. Analysis of acoustic signals emitted during different laser regimes, such as conduction, keyhole, etc. in time, frequency and time-frequency domains could provide quantitative information about the underlying physical mechanisms. This article reports a statistical analysis of the features in acoustic signals to perceive the characteristics of failure modes occurring during layering of stainless steel 316L. The visualization of the feature space distribution that corresponds to different failure modes shows the potentials of applying machine learning for in situ classification. The paper also proposes strategies in terms of data acquisition and preprocessing for building a comprehensive monitoring system. (c) 2020 The Authors. Published by Elsevier B.V.