In the semiconductor industry, process monitoring has been recognized as a critical component of the manufacturing system. Multivariate statistical process monitoring (SPM) techniques, such as multiway principal component analysis and multiway partial least squares, have been extend to monitor semiconductor processes. These SPM methods require extensive, often off-line data preprocessing such as data unfolding, trajectory mean shift, and trajectory alignment. This requirement is probably not an issue for the traditional chemical batch processes but it poses a significant challenge for semiconductor batch processes. This is because data preprocessing makes model building and maintenance extremely labor intensive due to the large number of models in a typical semiconductor fab. In addition, semiconductor process data often show more severe nonnormality compared to those of the traditional chemical process under closed-loop control, which results in suboptimal performance in many applications. To address these challenges, several pattern classification based monitoring (PCM) methods have been developed recently, but some limitations remain and trajectory alignment is still required. In this article, we analyze the fundamental reasons for the limitations of the SPM and PCM methods when applied to monitor semiconductor processes. In addition, we propose a new statistics pattern analysis (SPA) framework to address the challenges associated with semiconductor processes. By monitoring batch statistics, the proposed SPA framework not only eliminates all data preprocessing steps but also provides superior fault detection performance. Finally, we use an industrial example to demonstrate the advantages of the proposed SPA framework, and examine the fundamental reasons for the improved performance from SPA. (C) 2010 American Institute of Chemical Engineers AIChE J, 57: 107-121, 2011