Transformer outages significantly impact the reliability and cost efficiency of power systems. Studies indicate that approximately 30% of transformer failures stem from issues with on-load tap changers (OLTC), crucial components in transformer operation. Therefore, continuous monitoring of OLTCs is essential to enhance transformer serviceability. In this study, a vibro-acoustic signal analysis-based monitoring system is employed to assess the condition of OLTCs. This system has been operational since 2016 on three single-phase autotransformers within the Hydro-Québec network, continuously measuring vibration signals from their OLTCs. Notably, these transformers are equipped with sister OLTC units, and the system also records temperature and other pertinent parameters. To detect anomalies in OLTCs and analyze the generated vibration signals, a convolutional variational autoencoder (CVAE) is utilized, trained individually for each transformer family. This approach allows mapping the signal envelope into a two-dimensional latent space using the encoder component of the CVAE, facilitating visual investigation and analysis. The decoder component reconstructs the original input from data in the latent space. Several thresholds based on reconstruction errors are evaluated to detect anomalies, achieving optimal thresholds for each family. This results in anomaly detection rates of 4%, 5%, and 2%, respectively, when tested on data from within the same family not used in the training phase. Furthermore, when tested on data from the other two families, the anomaly detection rates are 99%, 99%, and 100%, respectively. These findings underscore the methodology’s accuracy and effectiveness in identifying anomalies in OLTC operations and distinguishing between different transformer families. Consequently, it holds promise for preemptively identifying potential future anomalies.