Discriminating clear-ocean from cloud in the thermal IR imagery is challenging, especially at night. Thresholds in automated cloud detection algorithms are often set conservatively leading to underestimation of the Sea Surface Temperature (SST) domain. Yet an expert user can visually distinguish the cloud patterns from SST. In this study, available pattern recognition methodologies are discussed and an automated SST Pattern Test (SPT) is formulated. Analyses are performed with SSTs retrieved from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor onboard S-NPP using the NOAA operational Advanced Clear-Sky Processor for Oceans (ACSPO) system. Based on the analyses of global data, we have identified spatial features potentially useful for discriminating cloud from clear-ocean. The SPT attempts to mimic the visual perception by a human operator such as gradient information, spatial connectivity, and high/low frequency discrimination. It first identifies contiguous areas with similar features, and then makes a decision based on the statistics of the whole region, rather than on a per pixel basis. The initial objective of the SPT was to automatically identify clear sky regions misclassified by ACSPO clear sky mask as cloudy, and improve coverage in dynamic areas of the ocean and in the coastal zones. Future work will be directed towards extending the SPT to also minimize cloud leakages, and redesigning the current ACSPO clear-sky mask making full use of pattern recognition approach.