In recent years, transfer learning has gained more and more attention. However, most of the existing online transfer learning methods transfer knowledge from a single source, and it is hard to make effective transfer learning when the similarity between source domain and target domain is low. To solve this problem, this paper proposes a multi-source online transfer learning method, LC-MSOTL, based on local classification accuracy. LC-MSOTL stores multiple classifiers each trained on a different source, computes the distance between the new arrived sample and its k-nearest neighbor samples in the target domain as well as the local classification accuracies of each source domain classifier on the k-nearest neighbor samples, selects the classifier with the highest local classification accuracy from source domain classifiers and combines it with the target domain classifier, so as to realize the knowledge transfer from multi-source domains to a target domain. Experiments on artificial datasets and real datasets illustrates that LC-MSOTL can effectively transfer knowledge selectively from multi-source domains, and can get higher classification accuracy compared with the single source online transfer learning algorithm OTL.