A methodology for pipeline integrity monitoring systems using a mixture of clustering and classification tools for fault detection is presented here. The approach is used to classify more readily faults or changes in the context of on-line leak detection with initially off-line training. The methodology is applied to a small-scale pipeline monitoring case where portability, robustness and reliability are amongst the most important criteria. The results are encouraging as relatively low levels of false alarms and increased fault detection are obtained.