This paper presents a fully automatic system intended to detect leaks of dielectric fluid in underground high-pressure, fluid-filled (HPFF) cables. The system combines a number of artificial intelligence (AI) and data processing techniques to achieve high detection capabilities for various rates of leaks, including leaks as small as 15 l per hour. The system achieves this level of precision mainly thanks to a novel auto-tuning procedure, enabling learning of the Bayesian network – the decision-making component of the system – using simulated leaks of various rates. Significant new developments extending the capabilities of the original leak detection system described in 1 and 2 form the basis of this paper. Tests conducted on the real-life HPFF cable system in New York City are also discussed.