In this paper, we propose a novel framework for electrical appliances identification using statistical harmonic features of current signals and the use of the k-NN classifier combined with a voting rule strategy. Harmonic coefficients are computed over time using short-time Fourier series of the current signals. From these sequences of coefficients, the mean, standard deviation, skewness, and kurtosis are computed, which provide the statistical harmonic features. This framework has three novelties: (i) selecting the best combination of statistical measures in the sense of classification rate (CR); (ii) combining the k-NN classifier with the voting rule method in order to search for the best number of voting vectors; and (iii) selecting relevant features for the task of appliances identification by using one of the relevant feature selection algorithms based on mutual information. Results evaluated on the Plaid dataset clearly show that the mean and standard deviation statistics combination gives the best CR of 92% with 500 features and gives the minimal computing time compared to the system based on HMM models. Moreover, combining the k-NN classifier with the voting rule using the above features increases the CR up to 95%. Using this combination, the results also show that an increase of the training dataset size further improves identification performance results in terms of precision, sensitivity, and F-score. A feature selection procedure based on joint mutual information strategy shows that using a selected subset of five features is sufficient, giving similar CR results to those obtained using the total number of features, whatever the training dataset size.