Birds are an important part of the ecosystem. Knowing the number of existing bird species is important for maintaining ecological balance. The sounds of birds contain rich biological features. The classification of bird species based on their sounds helps to protect the ecological environment. In this study, a fused classification method based on the Error Correction Output Coding (ECOC) and Support Vector Machines (SVM) is investigated to classify 11 species of birds. The Mel Frequency Cepstrum Coefficients (MFCC) of bird sound are extracted as acoustic feature. The ECOC-SVM is compared with Random Forest (RF), Gaussian Mixture Model (GMM) and a multi-layer perceptron neural network (MLP-NN) and a convolutional neural network (CNN) for the bird sound classification. The proposed ECOC-SVM model is validated on the open dataset available from the Macaulay Library at Cornell University. The ECOC-SVM achieves the best classification result and relatively low computational cost among all discussed classifiers on the test set with 100 % accuracy and 52 ms prediction time. The results show that the proposed ECOC-SVM model could well apply on the bird sound classification of multiple species with excellent performance and relatively low computational cost.