The purpose of this study is to determine the feasibility of a mesoscale (,300 km) cloud classification using infrared radiance data of satellite-borne instruments. A new method is presented involving an index called the diversity index (DI), derived from a parameter commonly used to describe ecosystem variability. In this respect, we consider several classes of value ranges of standard deviation of the brightness temperature at 11 mm (s BT). In order to calculate DI for 1286128 km 2 grids, subframes of 8 km68 km are superimposed to the satellite image, and then s BT is calculated for all 256 subframes and assigned to one of the classes. Each observed cloud pattern is associated with an index characterized by the frequency of s BT classes within the scene, representative of a cloud type. Classification of different clouds is obtained from Advanced Very High Resolution Radiometer (AVHRR)-NOAA 16 data at 1 km resolution. Stratus, stratocumulus and cumulus are specifically recognized by this window analysis using a DI threshold. Then, a six-class scheme is presented, with the standard deviation of the infrared brightness temperature of the entire cloud scene (s c) and DI as inputs of a neural network algorithm. This neural network classifier achieves an overall accuracy of 77.5% for a six-class scheme, and 79.4% for a three-class scheme, as verified against the analyses of nephanalists as verified against a cloud classification from Météo France. As an application of the proposed methodology, regional cloud variability over Pacific is examined using cloud patterns derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) carried aboard Earth Observing System (EOS) Terra polar orbiter platform, for February 2003 and 2004. The comparison shows regional change in monthly mean cloud types, associated with 2003 El Niñ o and 2004 neutral events. A significant increase in the occurrence of convective clouds (+ 15%) and a decrease in stratiform clouds (210%) are observed between the two months.