The recognition of transient overvoltage characteristics is the premise of disturbance compensation of the transient overvoltage. Based on that, the recognition algorithm of transient overvoltage characteristics based on symmetrical components estimation was proposed. The generation mechanism of the transient overvoltage in gas insulated switchgear (GIS) was analyzed. Then, the transient overvoltage was measured via the capacitive sensor method. The three-phase voltage of ultra-high voltage grid was asymmetrical when the transient overvoltage appeared. At present, the asymmetrical three-phase voltage was decomposed into the superposition of a symmetrical positive-sequence component, a negative-sequence component, and a zero-sequence component via the symmetrical components estimation to build the superposition model. The model was decomposed via the trigonometric identity and the modified neural network of the least mean square learning rule was used to estimate the parameter vector of the characteristic quantity of the transient overvoltage in real time. The feasibility of the proposed algorithm was verified via comparing the simulation of the proposed algorithm and the algorithm based on dp transformation. The experimental results show that the proposed algorithm has the advantages of a small operand, high detection precision, and fast action.