Metal additive manufacturing is a recent breakthrough technology that promises automated production of complex geometric shapes at low operating costs. However, its potential is not yet fully exploited due to the low reproducibility of quality in mass production. The monitoring of parts quality directly during manufacturing promises to solve this problem, while machine learning showed efficient performance correlating versatile manufacturing measurements with different quality grades. Today, most monitoring algorithms are based on semi- or supervised learning, thus, requiring a collection and ground-truth validation of training sets. This is costly and time consuming in real-life conditions. Our work is a feasibility study of the application of unsupervised machine learning to monitor different manufacturing regimes and quality in metal additive manufacturing. The algorithm combines the kernel Bayes rule for inference and Bayesian adaptive resonance for structuring the incoming data. Airborne acoustic emission from laser powder bed fusion is used as an algorithm input. The recognition of the main manufacturing regimes (conduction mode, stable, and unstable keyholes) are shown on real-life data, while the self-learning accuracy of developed algorithm exceeds 88 %. Our approach promises future development of plug-and-play quality monitoring systems for laser processing technology, requiring minimum modifications of the existing machines, reducing time/cost for algorithm preparation and providing continuous data driven adaptation of the algorithm to changes in manufacturing conditions.