The paper presents an approach to the development of an intelligent system for real-time prediction of blast-furnace process states. The forecast is based on the analysis of image video sequence obtained as a result of stream video cameras installed on the tuyeres of a blast furnace. Algorithms for recognition of video images of tuyere hearths, as well as scenario forecasting of the evolution of technological situations, are proposed, The research aimes at the analysis of blast-furnace production situations and the prediction of their evolution in real time. This enables timely decisions on adjusting the control in automatic or automated mode. Based on the revealed patterns in the change of video data and by means of the algorithm developed by the authors for process dynamics analysis and prediction, a method is proposed, for early detection of abnormal situations at tuyeres, including the ones enatailing blast furnace destabilization process. Not only the process state at the next time instant is predicted, but also the most probable chain of several subsequent states. For the associative pattern search, Markov chains, machine learning and wavelet analysis methods are used.