<p>Dam monitoring is crucial for behavior analysis and safety assessment. The data recorded by monitoring systems are the basis of behavioral models. Ensuring the quality of these data is vital for making informed decisions and improving prediction accuracy. However, monitoring data often contain errors that need to be corrected before use. As data acquisition systems become increasingly automated, the resulting large databases present challenges that conventional methods cannot effectively address. This work proposes a methodology based on autoencoders for the automatic detection of outliers in dam monitoring data. The model is calibrated with a general procedure, which showed to be effective in all situations considered. The application of this method to data series from deformations, piezometers, joint openings and seepage flow in two different dams demonstrates its ability to detect all strong outliers without false positives. Only a few potential outliers, some of which are hard to classify after an exploratory analysis, were overlooked by the model. While the final decision on record validation should be made by experienced technicians, this approach can effectively screen large databases of measurements, aiding in the efficient identification of outliers.</p>