A method for identifying the topology of an electric power network, the electric power network comprising a slack node, a set of other nodes (N2,..., N3), a slack branch connected to the slack node, and a set of other branches (L1, L2) arranged for connecting the slack node and the other nodes one to another, and the electric power network further being provided with a monitoring infrastructure comprising metering units (M1,..., M7), one of the metering units being arranged for measuring the electric current flowing into, or out of, the slack node though the slack branch, and the other metering units (M2,..., M7) each being arranged for measuring the electrical current flowing into, or out of, one of said nodes through at least one of said other branches, the monitoring infrastructure comprising a processing unit connected to a communication network, the metering units being connected to the communication network so as to allow for data transmission to and from the processing unit, the method being characterised by the steps of: I. have the metering units (M1,..., M7) measure at the same time, repeatedly over a time window ( τ), current intensity values I ( t), and timestamp t ∈ { t 1,..., t m} the current intensity values; II. compute variations Δ I i ( t) of the current intensity measured by each metering unit, by subtracting from each current intensity value the precedent current intensity value, and compile chronologically ordered tables of the variations of the current intensity measured by each metering unit (M1,..., M7); III. perform a Maximum Likelihood Estimation of the current sensitivity coefficients KII i, j linking current variations Δ I i ( t) measured by each metering unit (M1,..., M7) to the current variations Δ I i ( t) ( j ≠ i) measured by the other measuring units as compiled during step II, while taking into account negative first-order serial correlation between error terms corresponding to discrepancies between the actual current variations (Δ I i ( t)) and the variations predicted by the Maximum Likelihood Estimation, and obtain from the estimated current sensitivity coefficients KII i,j a current sensitivity coefficient matrix; IV. Compute the network incidence matrix A N x( N― 1) from the current sensitivity coefficients estimated in step III based on the general assumption that the metering unit that is the most sensitive to the variations of the measured current in a branch connected to a particular node is the metering unit that is arranged at the physical node immediately upstream form said particular node. I. have the metering units (M1,..., M7) measure at the same time, repeatedly over a time window ( τ), current intensity values I ( t), and timestamp t ∈ { t 1,..., t m} the current intensity values; II. compute variations Δ I i ( t) of the current intensity measured by each metering unit, by subtracting from each current intensity value the precedent current intensity value, and compile chronologically ordered tables of the variations of the current intensity measured by each metering unit (M1,..., M7); III. perform a Maximum Likelihood Estimation of the current sensitivity coefficients KII i, j linking current variations Δ I i ( t) measured by each metering unit (M1,..., M7) to the current variations Δ I i ( t) ( j ≠ i) measured by the other measuring units as compiled during step II, while taking into account negative first-order serial correlation between error terms corresponding to discrepancies between the actual current variations (Δ I i ( t)) and the variations predicted by the Maximum Likelihood Estimation, and obtain from the estimated current sensitivity coefficients KII i,j a current sensitivity coefficient matrix; IV. Compute the network incidence matrix A N x( N― 1) from the current sensitivity coefficients estimated in step III based on the general assumption that the metering unit that is the most sensitive to the variations of the measured current in a branch connected to a particular node is the metering unit that is arranged at the physical node immediately upstream form said particular node.