The Radiometric Camera-by-Camera Cloud Mask (RCCM) is archived at the NASA Langley Distributed Active Archive Center as one of the standard products from the Multi-angle Imaging SpectroRadiometer (MISR) mission. The RCCM algorithm applied over land surfaces uses an Automated Threshold Selection Algorithm (ATSA) to derive thresholds that are applied to a cloud masking test to determine whether a given image pixel is clear or contains cloud. In this article, we established a framework for the selection of ATSA and the cloud masking tests, which is not only suitable for the RCCM over land, but cloud detection for other satellite missions. Using this framework, we have undertaken the largest comparison of existing histogram-based ATSAs (16 in total) and applied them to four cloud masking tests that can be constructed from the MISR radiances, namely the red channel bidirectional reflectance function (BRF), the standard deviation (STDV) of the red channel BRF, the normalized difference vegetation index (NDVI), and a parameter D that is constructed by optimizing the information from NDVI and red channel BRF for cloud detection. The cloud masking tests and ATSAs are applied to 35 MISR scenes from six snow-free land surface types. To evaluate their performance, reference cloud masks are constructed for the 35 scenes using interactive, supervised learning, visualization software. Independent of the ATSA and as a single cloud masking test, D performed the best in terms of having the lowest misclassification rate using the best possible threshold, the highest bimodal rate in the shape of the histograms derived from the 35 scenes, and the least sensitivity to errors in the choice of threshold. Of the 16 ATSAs, the methods of Li and Lee [Li, C.H., and Lee, C.K., (1993). Minimum cross-entropy thresholding. Pattern Recognition, 26(4), 617-625.] and Pal and Bhandari [Pal, N. R., and Bhandari, D., (1993). Image thresholding: some new techniques. Signal Processing, 33, 139–158.] performed the best when applied to D, with essentially unbiased performance and a root mean square of 15% when compared to cloud masks using the best possible thresholds. It is recommended that increased performance of the RCCM-land algorithm can be had through an increase in the space–time sampling used to generate histograms of D and the addition of a STDV cloud masking test to improve the detection of small cumulus clouds.