The current research work investigates the possibility of using machine learning models to deduce the relationship between WAAM (wire arc additive manufacturing) sensor responses and defect presence in the printed part. The work specifically focuses on three materials from the nickel alloy family – Inconel 718, Invar 36 and Inconel 625, and uses three sensor responses (welding voltage, welding current and welding audio) for predictions. A variety of types of prints, including ramp tests, single bead depositions, and walls were explored. Three different machine learning models are used – artificial neural networks (ANNs), K-Means clustering and random forests (RF), and the performances are compared. In addition to separate material analysis, cross-material predictions are conducted using two supervised models to investigate the prediction capabilities of such an approach. The results indicate that models are indeed capable of finding connections between welding parameters and defect formation, and the accuracies range from 60% to 90% and the correlation coefficient is less than 0.5 (indicating weak positive correlation) depending on the model and material. The cross-material predictions are significantly worse, with accuracies ranging from 20% to 27% and very weak correlation coefficients (less than 0.1). Analysis of the results indicates that the importance of audio sensor response depends on the nature of defect, and that additional sensors like spectrometers could give a wider range of information to cover more types of defects, potentially raising the performance of cross-material predictions. Between the models, random forest is found to perform the best overall, with ANNs coming in a close second. The versatility of ANNs indicates that increasing the dataset size and resolving the class imbalance could potentially tip the scales in the favor of ANNs.