In this work, we present a model-based method for reliably detecting Reverse Pilot faults within cable amplifier networks. This method has the advantage over traditional fixed bound fault detection techniques in that it is able to compensate for changes in the environmental conditions and, hence, reduce the occurrence of false alarms.Cable television distribution networks are used to distribute cable signals from a centrally located injection site to subscribers' homes. Typically, cable amplifier plants are two way asymmetrical communication networks. Traditionally, the upstream path has been used to transmit the status data from the trunk amplifiers to the head-end. More recently it is used to provide a high-speed data path for use in internet and other interactive services. Hence the ability to detect the occurrence of faults in the reverse path has become of paramount importance.We have implemented a general approach based on the use feedforward neural networks to model the behaviour of the Reverse Pilot of cable television amplifiers. This technique was able to provide good temporal localization of the start of fault conditions and a clear indication of the presence of the fault through its occurrence.