Abstract: Abstract: Catch per unite of effort (CPUE) is often used as an index of relative abundance in fisheries stock assessments. However, the trends in nominal CPUE can be influenced by many factors in addition to stock abundance, including the choice of fishing location and target species, and environmental conditions. Therefore CPUE standardization is a basic work in stock assessment and management. CPUE standardization research is a rapidly developing field, and many statistical models have been used in this field. Improvement of data quality and continued evaluation of model performance should be given priority so as to provide recommendation for management and conservation. In this paper, we evaluated the performance of 5 candidate methods (artificial neural network (ANN), regression trees (RT), random forest (RF), support vector machine (SVM) and generalized linear model (GLM)) using the actual fishery data for bigeye tuna (Thunnus obesus) from the International Commission for the Conservation of Atlantic Tunas (ICCAT). Statistical performances of these 5 models were compared based on mean square error (MSE), mean absolute error (MAE), 3 kinds of correlation coefficients (the Person's, Kendall's rank and Spearman's rank) and normalized mean square error (NMSE), which were measured by the difference between the observed and the corresponding predicted values. The results showed that the performance of the SVM was better than (or equivalent to) the RF, and their MSE, MAE, 3 kinds of correlation coefficients and NMSE were almost the same. These 2 algorithms were superior to the other methods based on the results from the training and testing dataset and all data, except the NMSE value in training dataset. The NMSE value of the RT was better than the SVM and RF. The performance of the RT was better than that of the ANN, but inferior to that of the SVM and RF except the NMSE value in training dataset. The performance of the ANN was better than that of the GLM. The performance of the GLM was almost the lowest in all the models, which suggested the performance of the traditional statistical method (GLM) was inferior to the other nonlinear statistical models in fishery data CPUE standardization. The annual trends of the standardized CPUE from the ANN, RT, RF and SVM models were similar to nominal CPUE from 2001 to 2013. But the annual trends of the GLM did not coincide with nominal CPUE. The average CPUE for the SVM method was almost always lower than that of the nominal CPUE value from 2001 to 2013. In this regard, because the more important and essential point was the comparison of three parameter selection in the testing data based on the validation, it was concluded that the SVM and RF were the best methods in fishery data CPUE standardization. The SVM and RF should be considered as potential statistical methods for fishery data CPUE standardization in fisheries stock assessment and management.