The feasibility of steel materials classification by support vector machines (SVMs), in combination with laser-induced breakdown spectroscopy (LIBS) technology, was investigated. Multi-classification methods based on SVM, the one-against-all and the one-against-one models, and a combination model, are applied to classify nine types of round steel. Due to the inhomogeneity of steel composition, the data obtained using the one-against-all and one-against-one models were ambiguous and difficult to discriminate; whereas, the combination model, was able to successfully distinguish most of the ambiguous data and control the computation cost within an acceptable range. The studies presented here demonstrate that LIBS-SVM is a useful technique for the identification and discrimination of steel materials, and would be very well-suited for process analysis in the steelmaking industry.