We propose a simple quality control procedure for micrometeorological datasets focused on removing the most common problems known to affect them using only raw data (ie, without calculating fluxes) and simple tests. Given that this quality control was motivated by the need to process large amounts of data produced by the Amazon Tall Tower Observatory (ATTO) project, we opted to implement fast-to-execute tests over computationally costly ones. This characteristic, which is often overlooked by quality control procedures, is important in some cases since runtime can be an issue when dealing with very large datasets. As an example, we applied our proposed quality control to a 10-month period ATTO dataset. The procedure implemented successfully flagged all situations where a subjective analysis would have detected the usual errors and problems in the dataset. Our results suggest that the most frequent issue with this dataset is the fact that sensor resolution is insufficient to measure fluctuations under low turbulence conditions, more specifically the virtual temperature. This issue was responsible for excluding roughly 66% of our data.