Manual arc welding is highly dependent on operator skill, leading to variability in weld quality and an increased risk of defects; therefore, reliable monitoring methods for Shielded Metal Arc Welding (SMAW) are required, particularly in manual environments where process variability and environmental noise are inherent. This study proposes a monitoring approach for classifying SMAW process parameters using airborne acoustic signals generated by the welding arc. Welding experiments were conducted on carbon steel plates of different thicknesses (3, 6, and 12 mm) using E6010, E6011, E6013, and E7018 electrodes under Alternating Current (AC) and Direct Current (DC) configurations; acoustic signals were recorded in real time and processed using Mel-Frequency Cepstral Coefficients (MFCCs) and deep audio embeddings from pre-trained VGGish and YAMNet models as inputs to artificial neural network classifiers for multi-output classification of welding process parameters. Model performance was evaluated using per-target metrics (accuracy and macro F1-score) and joint multi-output metrics (Exact Match and Hamming Accuracy). MFCC-based models significantly outperformed embedding-based approaches, achieving up to 94.51% Exact Match and 97.88% Hamming Accuracy, while reducing computational costs. These results demonstrate the feasibility of SMAW monitoring using arc sound, suggesting that spectral features are an effective solution for welding-process monitoring and a promising foundation for future weld-quality monitoring systems.