Many different models for CO2 corrosion exist in the oil and gas industry. However, these models lag significantly behind the needs of the industry. There is still a large knowledge gap between actual processes occurring in the field and the current mechanistic and empirical models of CO2 corrosion. The complexity of the underlying physico-chemical phenomena is often such that our understanding is significantly lower than the level required for the mechanistic modeling. There is a need to develop a model that would have both the capability to predict the CO2 corrosion rate with high accuracy, as well as provide knowledge that would aid the understanding of the phenomena. This thesis focuses on the development of an Artificial Neural Network model based on CO2 field data used in predicting the corrosion rate of carbon steel. Further, rules are extracted from the trained network using a TREPAN decision tree algorithm to translate the hypothesis learnt into symbolic form. The performance of the neural network model is compared to a linear regression model using MINITAB. The efficacy of the rule set is then compared to the C4.5 machine learning algorithm. The interrelationship of input variables is discussed based on the constructed network model and the generated rule set.