Independent component analysis (ICA) and support vector machine (SVM) techniques were used to identify the fuel types. Flame oscillation signals were captured by a flame monitor. Thirty flame features were extracted from each flame oscillation signal to form an original feature vector. The ICA technique was applied to choose the independent flame features from each original feature vector. An SVM model was deployed to map the flame features to an individual type of fuel. The results obtained by using eight different types of coal demonstrated that the ICA technique combining with a well trained SVM can be used for identifying the fuel types, and the average success rate was 96.2% in 20 trials. The ICA preceded by principal component analysis (PCA) used for whitening and dimension-reducing performed a bit better than individually using the ICA technique, and the average success rate of fuel type identification was 97.8% in 20 trials.