An artificial neural network (ANN) model was developed to predict the onset of failure of glass fibre reinforced epoxy composite pipes under multiaxial loadings. The developed ANN model used input/output experimental data for training and classification. The model was expected to predict the first-ply failure within the pipe composite laminates under various biaxial stress ratios. The biaxial failure envelope was then illustrated by plotting the failure points in a graph showing axial stress versus hoop stress. During the model’s construction, the best entire mean classification accuracy rate achieved was within the range of 95%–99.66%. Validation with experimental findings indicated good agreement with the model’s predictions, with less than 30% variation. The results suggest that the ANN model can be extended to yield useful predictions of the onset of failure in composite pipes under a range of stress conditions. This can be utilised as an internal means for pipe rating prior to the required standard ASTM qualification process.