Accuracy of vital signs monitoring systems depend on the quality of the measurements by the sensors. Crude techniques are applied to discard measurements which have a low signal-to-noise ratio or are saturated. A more accurate and flexible technique enables more measurements to be kept, more meaningful signal quality information to be extracted, more accurate vital signs extraction and more systems to readily embed signal quality assessment in the signal processing pipeline. Improvements include preprocessing of the signal that is independent of variations of underlying hardware, use of features with low computational complexity and high predictive power, cross-channel feature extraction, application of a trained machine learning model, and flexible translation of signal quality classification information into a continuous metric for signal quality.