The feasibility of acoustic emission structural health monitoring to detect, localise and monitor crack propagation during quasi-static mode I loading of adhesively bonded joints was studied. Unsupervised artificial intelligence pattern recognition methods (Self-Organised maps and K-means) were used to classify acoustic emission raw data as either background noise or relevant information. After that, three different time-of-arrival picking algorithms were considered and implemented to determine the acoustic emission source's location, and their accuracy was discussed. Localised acoustic emission events were divided into well-defined groups with different energy levels and compared to Digital Image Correlation and visual evaluation results. It was possible to conclude that the highest energetic group allows the assessment of the onset of plasticisation ahead of the crack-tip within the studied adhesive, bringing novel standpoints to the use of acoustic emission as a structural health monitoring method for adhesively bonded joints.@en