The work explores the possibility of detection and location of the pipe burst inside an isolated part of the water supply system, i.e. district metered area, based on the known values of five parameters. These parameters were one variable of flow, one variable of pressure at the inlet of the system and three variables of pressure measured at different locations within the system. The measured values of all five parameters were collected and recorded continuously in time of one year. For the research, we used data on the values of flow and pressure for hours and days in the period of one year, in a manner that the hydraulic model simulated hydraulic conditions with and without pipe burst with different leakage flows. These data were used as inputs in neural networks; while the primarily considered output value was the state of burst, and the secondary, if the bursts occurred, output values were the spatial coordinates of burst location. After the simulation of a number of different conditions with and without a pipe burst in the water supply network, it was found that, managing well trained neural networks, selected parameters could be used to provide information of occurrence of pipe burst. Neural networks modeled for the purpose of locating pipe burst showed that, when flow formed due to pipe burst increases, error in determining the location of burst drops. However, we could not obtain sufficient decrease of this flow-error correlation line in order to achieve acceptable pipe burst location error for particular realistic pipe burst flow.