This paper proposes the use of power signature to recognize different loads with the same real power and reactive power in a non-intrusive load-monitoring (NILM) system. To test the performance of the proposed approach, the data sets for electrical loads were analyzed and established using an electromagnetic transient program (EMTP) and onsite load measurement. Load recognition techniques were applied in a neural network. The effectiveness of load recognition and the time requirement were analyzed and compared using a back propagation classifier method. The experiments revealed that analyzing the turn-on transient energy signatures can enhance the efficiency of load recognition, particularly for different loads with the same real power and reactive power in a NILM system, and improve ability of computational speed.