Abstract: To address the problems of single feature extracted and low classification accuracy in bird sound recognition algorithms, a bird sound recognition method based on hybrid feature selection and Gray Wolf algorithm-optimized kernel limit learning machine is proposed. Firstly, the large-scale acoustic feature set ComParE is extracted from bird sound data, secondly, the F_score of each feature is calculated and ranked, then the generalized sequential forward floating search (GSFFS) is used as the search strategy, and the correct rate of feature subsets on the kernel limit learning machine (KELM) with ten-fold cross-validation is used as the feature selection criterion to select the features applicable to bird sound recognition subset, and finally the optimal KELM parameters are selected by the Grey Wolf Optimizer (GWO) to recognize bird sounds. In the experiments conducted in the bird sound database of the Berlin Museum of Natural Sciences, the average correct rate and F1-score of the method reaches 94.45% and 92.29% for 60 types of bird sounds. The results show that the method has higher recognition accuracy than the traditional self-designed single feature set, and the GWO-KELM model is easier to find the global optimal value than the grid search method.