Support vector machines (SVMs) have become a popular tool for discriminative classification. Powerful theoretical and computational tools for support vector machines have enabled significant improvements in pattern classification in several areas. An exciting area of recent application of support vector machines is in speech processing. A key aspect of applying SVMs to speech is to provide a SVM kernel which compares sequences of feature vectors--a sequence kernel. We propose the use of sequence kernels for language recognition. We apply our methods to the NIST 2003 language evaluation task. Results demonstrate the potential of the new SVM methods.