Based on the good texture expression abilities of the gray level co-occurrence matrix，a flow regime identification method based on gray level co-occurrence matrix and support vector machine was proposed.Gas-liquid two-phase flow images were captured by digital high speed video systems in a horizontal pipe.The image gray level co-occurrence matrix texture feature was extracted by using image processing techniques.Then，the image gray level co-occurrence matrix texture feature eigenvectors of flow regime were established.The support vector machine was trained by using those eigenvectors as flow regime samples and flow regime intelligent identification was realized.The test results showed that the support vector machine could quickly and accurately identify seven typical flow regimes of gas-water two-phase flow in the horizontal pipe.The whole identification accuracy was 100%，and an estimation of the process time was 1.7 s for flow online identification by using the new and effective method.