In this study, a grading system for ears of corn was developed to design sterilization process of the ear of corn using fuzzy c-mean (FCM) clustering. Three grades (A, B, and C) of corn found by FCM (the m-value and the cvalue were 1.15 and 3, respectively) were used to design the sterilization process of the ear of corn. The heat transfer simulation model was successfully developed to determine the locations of the cold point for the three different grades of corn during sterilization (RMSE = 0.104 degrees C). Among the 3 different temperature used in this study (111.1, 121.1, and 131.1 degrees C), 121.1 degrees C found to be the optimum sterilization temperature based on the analysis of texture properties of corn. The heating time to reach the target F-sigma-value (5 min) for grades A, B, and C was 61, 49, and 36 min, respectively. When the longest heating time (61 min) was applied, the quality of the overheated corn (grades B and C) was significantly decreased (p < 0.05), whereas the quality of the corn at the optimum sterilization condition for each grade showed no significant difference. The heat transfer simulation model was successfully combined with the grading system developed by FCM to optimize the sterilization process of packaged whole corn. The texture properties and sensory attributes of sterilized corn were improved by applying the optimum sterilization processes which was adjusted based on the size and weight of corn ears.