A method for automatic detection of burning through of short circuit CO 2 arc welding is presented. It is based on the extraction of arc signal features as well as classification of the obtained features using self organize feature map(SOM) neural networks in order to get the weld quality information, for example, to determine if there is defect in the product. This is important for the on line monitoring of weld quality especially in robotic welding and lay the foundation for the further real time control of weld quality. A method for automatic detection of burning through short circuit CO 2 arc welding is presented. It is based on the extraction of arc signal features as well as classification of the resulting features using self organize feature map (SOM) neural networks in order to get the weld quality information, for example, to determine if there is defect in the product. This is important for the on line monitoring of weld quality especially in robotic welding and lay the foundation for the further real time control of weld quality.