Deep convection over the ocean can present a significant hazard to aviation along transoceanic routes. These clouds are sometimes associated with severe turbulence, icing, strong vertical updrafts and lightning. Infrared (IR) geostationary satellite observations help to identify the locations of large cloud regions but alone do not provide insight into the internal cloud structure and are incapable of distinguishing clouds that contain hazards from those that are more benign. A Convective Diagnosis Oceanic (CDO) algorithm developed at the National Center for Atmospheric Research (NCAR) applies a fuzzy logic, data fusion technique to the outputs of three satellite-based convection detection algorithms to identify deep convective clouds. A verification approach to evaluate the CDO performance is presented in this study. The evaluation exercise was performed within a large domain that encompasses the Gulf of Mexico, western Atlantic Ocean and northern South America. Observations from the National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM) satellite in low earth orbit were used to verify the CDO performance. A space-borne Precipitation Radar (PR) and Lightning Imaging Sensor (LIS) aboard the TRMM satellite provide valuable information of the internal structure of deep convection. Clouds which contain reflectivity ≥ 30 dBZ in the mixed phase region, convective rain associated with cold cloud top temperatures (< -3° C), or the presence of lightning are the criteria used to determine deep and potentially hazardous convective clouds. Initial results indicate the CDO has skill at identifying deep convection but tends to exaggerate the presence of hazardous conditions. Quantitative evaluations will be presented at the meeting.