Gas metal arc welding (GMAW) process is one of the most important in the industry, so different efforts have been made to anticipate the parameters to convert this process into a stable one capable of joining parts with minimum human interference. In this sense, controlling is essential for automated applications because properties such as the weld mechanical strength are defined by the metal composition, the microstructure, and the weld bead geometry. Nevertheless, performing this automatic control to guarantee quality characteristics similar to a human expert’s in mechanized welding systems is still tricky. Nowadays, although various sensors have been used in the monitoring for control, it is still hard to detect effective options to real-time identify geometry characteristics in the formation process of the welds. Furthermore, even today, a process much more complex is to control more than one parameter simultaneously or control the weld penetration using a single sensor. Then, this research describes two intelligence systems for real-time control of the weld bead geometry in the GMAW process. The first is a passive vision system with sensor fusion that controls the width and height; the second is an active vision system that controls the penetration. Results indicate that the proposed methodology can be applied to simultaneously control external geometrical parameters without a predefined model of the welding process. In the case of penetration, a fuzzy controller and a neural network-based model help the system adapt to input parameter variations throughout the welding process, thus correcting instabilities under changing operating conditions.