<p>Vision-based monitoring of Wire Arc Additive Manufacturing (WAAM) using supervised deep learning represents the state of the art in anomaly detection, but such approaches require large labeled datasets that are costly to obtain and typically limited to laboratory conditions. To address these limitations, this work proposes a hybrid deep learning–statistical process monitoring (SPM) framework tailored to the stochastic nature of conventional arc welding processes such as GMAW-based additive manufacturing, where existing methods often overfit. The framework integrates a residual convolutional autoencoder (Res-CAE) with skip connections, which jointly analyzes video frames to generate refined latent-space features that are subsequently monitored using a Bayesian-optimized T² control chart. A novel quality index is further introduced to track component quality by accounting for the number, frequency, and severity of anomalies, moving beyond the conventional role of process interruption. Compared with state-of-the-art reconstruction-based anomaly detection methods, the proposed approach improves the F₂-score from 73.4% to 82.7%, achieves superior latent-space representation, and provides an interpretable quality metric with quantified uncertainty, offering a comprehensive solution for image-based monitoring of arc welding processes. This improvement is particularly relevant in short-circuit operating modes, where the strong process variability leads reconstruction-based deep models to overgeneralise and unintentionally reconstruct anomalous patterns, whereas the proposed SPM-guided representation preserves meaningful deviations and yields a more discriminative latent space.</p>