Edge welding of thin sheets is very difficult because of the fit-up problem and small weld area In laser welding, joint fit-up and penetration are critical for sound weld quality, which can be monitored by appropriate methods. Among the various monitoring systems, visual monitoring method is attractive because various kinds of weld pool information can be extracted directly. In this study, a vision sensor was adopted for the weld pool monitoring in pulsed Nd:YAG laser edge welding to monitor whether the penetration is enough and the joint fit-up is within the requirement. Pulsed Nd:YAG laser provides a series of periodic laser pulses, while the shape and brightness of the weld pool change temporally even in one pulse duration. The shutter-triggered and non-interlaced CCD camera was used to acquire a temporally changed weld pool image at the moment representing the weld status well. The information for quality monitoring can be extracted from the monitored weld pool image by an image processing algorithm. Weld pool image contains not only the information about the joint fit-up, but the penetration. The information about the joint fit-up can be extracted from the weld pool shape, and that about a penetration from the brightness. Weld pool parameters that represent the characteristics of the weld pool were selected based on the geometrical appearance and brightness profile. In order to achieve accurate prediction of the weld penetration, which is nonlinear model, neural network with the selected weld pool parameters was applied.