A method has been proposed for welding penetration status prediction in the paper. First, an experimental system was set up and welding experiments were performed. Some groups of welding images could been obtained. A composite filtering system composed of a neutral light reduction filter and a narrow band filter was developed to filter the weld arc disturbance. Some operations were performed to the images, namely the median filter and gray transformation. Then a neural network was setup, containing three layers. The inner widths of pool xn, the outer widths of pool xw, the width difference values e between the inner and outer of pool, ratios of inner pool widths Rn and ratios of outer pool widths Rw between two adjacent images were determined to be the input parameters. The penetration parameter p was chosen to be the output. Based on the images, groups of pool parameter data have been obtained and used to train the network. In this way, the weld penetration prediction model can be deduced. Finally, verification tests have been done. It showed that weld penetration situation predicted by the model is fit to its real condition. The accuracy rate is up to 96%, which affords a new way for penetration detection.