This paper aims to develop an Artificial Neural Network based controller to detect the saturation level in the magnetic core of a welding transformer. The magnetization level detector is a substantial component of a Middle-Frequency Direct Current (MFDC) Resistance Spot Welding System (RSWS). The basic circuit of a resistance spot welding system consists of an input rectifier, an inverter, a welding transformer, and a full-wave rectifier which is mounted on the output of the welding transformer. The presence of unbalanced resistances of the transformer secondary windings and the difference in characteristics of output rectifier diodes can cause the transformers magnetic core to become saturated. This produces spikes in the primary current and finally leads to the over-current protection switch-off of the entire system. To prevent the occurrence of such a phenomena, the welding system control must detect that the magnetic core is approaching saturation. In this paper,an Artificial Neural Network is designed whose only input is the primary current of the welding transformer. The proposed ANN is based on the dynamic model of the Resistance Spot Welding System. Before the ANN can be applied, its structure must be defined and an appropriate learning method must be adopted for its training. The ANN implemented in this paper is a three layered ANN and is trained using the resilient back-propagation algorithm. The ANN is trained so as to recognize the waveform of the current spikes in the primary current caused by the magnetic core saturation, which is then used for magnetization level detection.