Consumer's interest in food safety and quality is increasing, mainly due to the international food trade, which needs rapid and non-destructive inspection techniques. The computer vision system is highly correlated with the recent usage in the food and agriculture industry. Therefore, this study aimed to perform the computer vision system (CVS) to investigate the external quality of mechanically damaged bananas ‘Somali’ after 25 days of storage at three temperature conditions. Bananas were damaged by the drop impact test by dropping a stainless-steel ball of 110 g through a hollow pipe at 40 and 60 cm drop heights. Bananas with no damage were set as control. Bruised and non-bruised bananas were stored at 5, 13, and 22°C. A computer vision system was used to measure the external properties of bananas like color, surface area, and browning spots. The captured Red, Green, and Blue (RGB) images were analyzed by Image J software. The principal component analysis (PCA) was used to study the correlation between the studied parameters. The results of the study showed a high percentage in surface area reduction percentage in 60 cm bruised bananas stored at 22°C. Storage at 13°C showed the least changes in color and browning spots after 25 days of storage. The study can confirm the effectiveness and efficiency of using a computer vision system and image processing in determining the most common sensory attributes in perishable fruit like bananas.