Coal mills have a significant influence on the reliability, efficiency, and safe operation of a coal-fired power plant. Coal blockage is one of the main reasons for coal mill malfunction. It is highly essential to accurately detect the critical blockage in coal mills to ensure a safe and stable operation of the unit. Taking advantage of unsupervised learning methods and historical process data from distributed control systems (DCS), a stacked denoising autoencoder (SDAE) network-based approach for monitoring critical blockage in a coal mill is proposed in this work. The SDAE model with optimized parameters is applied to reconstruct the operating data during normal operating conditions. The intrinsic relationship between all input variables was captured by training a multilayer network model. The monitoring indicator was obtained from the reconstruction errors, and the threshold for monitoring indicators was obtained using kernel density estimation (KDE) during normal operation. The proposed network is independent of fault data, requires a reduced on-line calculation, and demonstrates a better real-time performance compared to conventional methods. The abnormal variables analysis may provide a theoretical evidence for critical blockage. The effectiveness of the proposed method is validated using operating data collected from an actual coal-fired power plant in China. The results demonstrated that the proposed method can effectively detect critical blockage in a coal mill and issue a timely warning, which allows operators to detect potential faults.