In order to identify the common-used household appliances, this paper proposes a kind of neural network which is effective to enhance the identification ability. First of all, based on load signature, aiming at harmonic characteristics of steady-state current in each electrical equipment, the feature tag is thereby established. Then, the RPROP neural network is adopted, which makes the input data feature nonlinearly map to output layer, and guides the neural network to converge to global optimal point rapidly. When training the neural network, the combined features are used to decompose the characteristics of electrical equipment. Finally, the experimental results of five common electrical appliances demonstrate that the proposed algorithm can effectively identify combined working states of household appliances, and it also can decompose the working states of electric appliances with similar power and little different harmonics.