Stormwater pipe networks are an important part of the water infrastructure that removes stormwater runoff from urban cities of Australia. This paper presents the attempts made to improve the classification or prediction of structural condition for individual stormwater pipes. Such predictive information can be used to prioritize pipes for inspection and subsequent maintenance actions and thus support water utilities implementing their proactive management strategies. In this paper, a support vector machine (SVM) was developed, tested and compared against the back‐propagation neural network (BPNN) model which has been used in a previous study. The developed model was applied to a case study using a sample of CCTV inspected pipes and corresponding pipe factors. The results showed that with regards to correctly predicting structural condition of individual stormwater pipes, the SVM model outperformed the BPNN significantly in the train dataset and marginally in the test dataset. Several advantages of the SVM model were also found compared to the BPNN model.