This paper discusses automatic modulation classification (AMC) of analog schemes. Histograms of instantaneous frequency are used as classification features and Support Vector Machines (SVMs) are then applied to classify the unknown modulation schemes. This novel machine-learning based method can insure robustness in a wide range of SNR. Extensive simulation has demonstrated the validity of the proposed AMC algorithm. It is a practical algorithm in blind AMC environments.