Keeping combustion stable and detecting unstable states in time is crucial for coal-fired furnaces such as rotary kilns, boilers, and oxygen furnaces. Because of the interference and complex conditions in the industrial field, recognition of combustion conditions by vision analysis is difficult. In this article, we propose a robust nonlinear dynamic system analysis-based approach for combustion condition recognition by extracting chaotic characteristics from a flame video. We first discover chaotic characteristics in the intensity sequence extracted from a flame video of coal-fired kilns, and then we further find that the underlying chaos rules differ between combustion conditions. Based on this finding, we design a set of trajectory evolution features and morphology distribution features of chaotic attractors for combustion condition recognition. After reconstructing the chaotic attractors from the intensity sequence of a flame video by phase space reconstruction, the quantified features are extracted from the recurrence plot and morphology distribution and put into a decision tree to recognize the combustion condition. The experimental results on real-world data show that the proposed method can recognize the combustion condition in coal-fired kilns effectively and promptly. Compared with other methods, the recognition accuracy is improved more than 5%.