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With the widespread use of zinc coated steels in the manufacture of high volume spot welded assemblies, such as the automotive body-in-white, there is a need to address the inherent difficulties in welding this particular product. The presence of the zinc coating increases the rate of degradation of the welding electrodes, and so there is a need for frequent electrode maintenance to combat the deterioration in weld quality associated with electrode wear. This results in short production runs and reduced productivity. Pre-programmed current stepping of the welding current may be used to compensate for the reduction in weld size with electrode wear, and so extend electrode life. However, this open-loop technique is difficult to optimise, particularly when welding zinc coated steel. In order to develop a feedback control system for current stepping, it is necessary to relate the weld diameter to some measurable parameter, in order to perform continuous monitoring of the weld quality. In view of the difficulty of deriving a suitable mathematical description of the physical process, on which to base a control algorithm, alternative techniques for spot weld quality monitoring and control have been examined. A neural network based model of the spot welding process has been produced, to predict weld quality from the measured electrical data. Guidelines have been developed for selecting the ideal network parameters for maximising the prediction performance over the life of the welding electrodes. In order to overcome the difficulties observed in optimising the pre-programmed current stepping control system, the feasibility of using a rule based fuzzy logic controller has been assessed. Rules were defined for determining the required step change in welding current to maintain weld quality, given the diameter of the previous weld and the estimated electrode tip diameter.