To overcome the complexities associated with combining or comparing multisensor data, a statistical gridding algorithm is introduced for projecting data from their unique instrument domain to a uniform space– time domain. The algorithm has two components: 1) a spatial gridding phase in which geophysical properties are filtered on the basis of a set of criteria (e.g., time of day or viewing angle) and then aggregated into nearestneighbor clusters as defined by equal-angle grid cells and 2) a temporal gridding phase in which daily statistics are calculated per grid cell from which longer time-aggregate statistics are derived. The sensitivity of the gridding algorithm is demonstrated using a month (1–31 August 2009) of level 2 Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) cloud-top pressure (CTP) retrievals as an example. Algorithm sensitivity is tested for grid size, number of days in the definition of a time average, viewing angle, and minimum number of observations per grid cell per day. The average CTP for high-level clouds from a number of different polar-orbiting instruments are compared on a global grid. With the data projected onto a single grid, differences in CTP retrieval algorithms are highlighted. The authors conclude that this gridding algorithm greatly facilitates the intercomparison of CTP (or any other geophysical parameter) and algorithms in a dynamic environment. Its simplicity lends transparency to understanding the behavior of a given parameter and makes it useful for both research and operational use.