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The focus of automatic solar-flare detection is on the development of efficient feature-based classifiers. The three principal techniques used in this work are multi-layer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) classifiers. We have experimented and compared these three methods for solar-flare detection on solar Hα images obtained from the Big Bear Solar Observatory in California. The preprocessing step is to obtain nine principal features of the solar flares for the classifiers. Experimental results show that by using SVM we can obtain the best classification rate of the solar flares. We believe our work will lead to real-time solar-flare detection using advanced pattern recognition techniques.