A fingernail imaging has been shown to be effective in estimating the finger pad forces along all three directions simultaneously in previous works. However, this method has never been used for the purpose of force measurement during a grasping task with multiple fingers. The objective of this paper is to demonstrate the grasp force-sensing capabilities of the fingernail imaging method integrated with a visual servoing robotic system. In this study, the fingernail imaging method has been used in both constrained and unconstrained multi-digit grasping studies. Visual servoing has been employed to solve the issue of keeping fingernail images in the field of view of the camera during grasping motions. Two grasping experiments have been designed and conducted to show the performance and accuracy of the fingernail imaging method to be used in grasping studies. The maximum value of root-mean-square (RMS) errors for estimated normal and shear forces during constrained grasping has been found to be 0.58 N (5.7%) and 0.49 N (9.2%), respectively. Moreover, a visual servoing system implemented on a 6-degrees-of-freedom (DOF) robot has been devised to ensure that all of the fingers remain in the camera frame at all times. Comparing unconstrained and constrained forces has shown that force collaboration among fingers could change based on the grasping condition.