Companies in the business of pipeline operations have as a basic assumption, operational security. Concerning this issue, there are some factors that could lead to accidents with material, environment and personal damage possibilities such as: internal and external corrosion, accidental excavations, improper operation that could submit the pipe to high pressures and third party interventions interested in commodities theft. This dissertation is aligned with pipeline companies real demands through the development of systems that could be used by these companies. Leak detection systems provided with leak location capabilities were developed to be used in liquid pipeline control centers, exploring non-conventional methods, besides the ones mentioned by API 1130. The leak detection systems developed were: mass balance, volume balance, fuzzy logic and neural networks. For the leak location systems the systems tested were: sonic velocity, hydraulic gradient and artificial neural networks. The products used were gasoline, diesel and fuel oil. On the issue of leak detection, the system based on neural networks detected simulated leakages, although there were some false indications. The system based on fuzzy logic presented good results, giving correct leak indications without any indication of false alarms, interpreting correctly the phenomena due to pipeline usual operations. The mass balance system has also presented good results, not generating false alarms, but detecting simulated leaks even with the pipeline in shut-in condition. To evaluate leak detection systems is common to conduct field tests that can be costly and take a long time to accomplish. A method for testing at a lower cost should be developed and a proposal is being shown in this work.