A support vector machine-based approach is presented for the identification of chaotic optical systems. The feasibility of this approach was demonstrated with the computer simulation through identifying a Bragg acoustooptic bistable chaotic system using a least squares support vector machine (LS-SVM). The proposed identification method was compared with the feed-forward neural network trained using back-propagation algorithm for the system identification. The LS-SVM possesses prominent advantages: over fitting is unlikely to occur by employing structural risk minimization criterion, the global optimal solution can be uniquely obtained owing to that its training is performed through the solution of a set of linear equations. Also, the LS-SVM needs not determine its topology in advance, which can be automatically obtained when training process ends. Thus its identifying accuracy and speed were found to be better than that of a conventional feed-forward neural network trained using back-propagation algorithm. This method is robust with respect to noise, and it constitutes another powerful tool for the identification of chaotic optical systems.