This paper discusses the development and validation of a cloud-screening algorithm for the AERONET (Aerosol Robotic Network) database, which is used to monitor aerosol optical depth globally. The algorithm aims to separate cloud-affected data from cloud-free data, a critical step in the process of standardizing measurements and processing for multiyear and large-scale comparisons. The procedure was tested on various geographical and optical conditions, including biomass burning events, hazy summer conditions, clean air from the Canadian Arctic, and variable cloudy conditions. The screening algorithm eliminates from 20% to 50% of the initial data, depending on cloud conditions. The paper details the methodology, including criteria for triplet stability, diurnal stability, smoothness, and three-standard deviation checks. The authors also discuss potential shortcomings and limitations of the algorithm, such as its inability to handle certain aerosol types and unusual events. The overall conclusion is that the cloud-screening algorithm effectively identifies cloud contamination while maintaining the integrity of daily averages and is applicable to any site within the AERONET network.This paper discusses the development and validation of a cloud-screening algorithm for the AERONET (Aerosol Robotic Network) database, which is used to monitor aerosol optical depth globally. The algorithm aims to separate cloud-affected data from cloud-free data, a critical step in the process of standardizing measurements and processing for multiyear and large-scale comparisons. The procedure was tested on various geographical and optical conditions, including biomass burning events, hazy summer conditions, clean air from the Canadian Arctic, and variable cloudy conditions. The screening algorithm eliminates from 20% to 50% of the initial data, depending on cloud conditions. The paper details the methodology, including criteria for triplet stability, diurnal stability, smoothness, and three-standard deviation checks. The authors also discuss potential shortcomings and limitations of the algorithm, such as its inability to handle certain aerosol types and unusual events. The overall conclusion is that the cloud-screening algorithm effectively identifies cloud contamination while maintaining the integrity of daily averages and is applicable to any site within the AERONET network.