Amidst the growing research on hand grasping interaction with products, understanding and accurately identifying grasps remain challenging. Previous methods, such as vision-based grasp posture analysis, electromyography, and data gloves with inertial, magnetic, and bending sensors, faced challenges including limited grasp comprehension, visibility constraints during identification, low accuracy, and lack of real-time identification. Conversely, tactile-based methodologies appear to offer a promising solution for grasp understanding and identification. This study aimed to estimate touch point distribution on fingers and palm across 33 hand grasps, utilizing a wearable tactile glove. Moreover, an algorithm-based methodology was developed for identifying hand grasps and determining their grasp similarity index. The findings displayed distinct touch point distributions for each grasp, with the palmar grasp exhibiting the highest touch point counts of 215 out of 240. Furthermore, the proposed methodology exhibited high accuracy in real-time hand grasp identification, with the grasp similarity index serving as an additional tool to validate accuracy. In conclusion, the ability to estimate touch point distribution and identify hand grasps holds significant potential for enhancing our understanding of hand-product interaction, with wide-ranging applications in product design, ergonomics, rehabilitation, prosthetics, robotics, and haptic devices.