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Laser welding is a rapidly developing technology that is of utmost importance in a number of industrial processes. The physics of the process has been investigated over the past 50 years and is mostly well understood. Nevertheless, online laser-quality monitoring remains an open issue until today due to its dynamic complexity. This paper is a supplement to existing approaches in the field of in situ and real-time laser-quality monitoring that presents a novel combination of state-of-the-art sensors and machine learning for data processing. The investigations were carried out using laser welding of titanium workpieces. The quality was estimated a posteriori by the visual inspection of cross-sections of the welded joints. Four quality categories were defined to cover the two main laser welding regimes: conduction and keyhole. The signals from the laser back reflection and optical and acoustic emissions were recorded during …