We use a cloud detection algorithm that detects cloudy pixels from MODIS images by characterizing individual pixels as cloudy or non-cloudy based on the brightness values (from 0 to 255) of the pixels and a predetermined threshold. The algorithm then produces mean fields of daytime cloudiness over different geographical regions. Although the cloud climatologies produced initially appeared realistic, it was found that the algorithm largely underestimated the cloud frequencies over some regions when using a threshold of 215. Examining many MODIS images and recording cloudiness over different sectors served as the “ground truth” data which we compared to the algorithm output. After comparing the subjective estimates and the algorithm output for four regions of the world, we found that the algorithm underestimates cloudiness over these additional regions and that lowering the thresholds to 170-190 over oceans and 190-215 over land generally identified the thick clouds most accurately. Although the thresholding technique is somewhat arbitrary, by better understanding how the algorithm behaves we can modify the algorithm to ensure that the output more accurately describes cloud climatologies around the world. If we are able to do this, then our algorithm could be used for many applications such as validating cloud climatologies generated from numerical model simulations or assessing climate change from historical satellite data.