Statistical process monitoring (SPM) is an important component in the long-term reliable operation of any system and its importance can only become greater in the era of smart manufacturing (SM). Previously we proposed statistics pattern analysis (SPA) based on the idea of using various statistics to quantify process characteristics, and monitoring these statistics instead of process variables themselves to perform process monitoring. In this work we provide a comprehensive review on recent progresses made in SPA framework, which underpins a roadmap of SPM we outlined recently. Both sample-wise feature extraction and variable-wise feature extraction are discussed, with new applications in both fault detection and diagnosis, and soft sensor development. Specifically, we provide the first systematic examination on the SPA's capability in handling process characteristics including dynamics, nonlinearity and data non-Gaussianity; and compare its performance to representative state-of-the-art SPM methods to highlight the enhanced capability of feature-based monitoring. In addition, the performance of SPA is tested using the benchmark industrial simulator TEP for fault detection and diagnosis, plus a wet lab and an industrial case studies for soft sensor development. Finally, the advantages and potential limitations of SPA in addressing the new challenges presented by smart manufacturing big data are discussed. (C) 2019 Elsevier Ltd. All rights reserved.