The anisotropy of soils has an important effect on stress-strain behavior. In this study, an attempt has been made to implement artificial neural network model for modeling the stress-strain relationship and predicting the undrained shear strength of normally consolidated clay with varying consolidation pressure ratios. The multi-layer neural network model, adopted in this study, utilizes the error back-propagation loaming algorithm. The artificial neural networks use the results of undrained triaxial test with various consolidation pressure ratios and different effective vertical consolidation pressure fur learning and testing data. After learning from a set of actual laboratory testing data, the neural network model predictions of the undrained shear strength of the normally consolidated clay are found to agree well with actual measurements. The predicted values by the artificial neural network model have a determination coefficient$(r^2)$ above 0.973 compared with the measured data. Therefore, this results show a positive potential for the applications of well-trained neural network model in predicting the undrained shear strength of cohesive soils.