Qualitative trend analysis (QTA) is a data-driven semi-quantitative technique that has been used for process monitoring and fault detection and diagnosis (FDD). Though QTA provides quick and accurate diagnosis—the increase in computational complexity of QTA with the increase in the number of sensors used for diagnosis—may prohibit its real-time application for very large-scale plants. In most of the chemical plants, the measurements are highly redundant and this redundancy can be exploited by performing principal component analysis (PCA) on the measured data. In this paper, we present a PCA–QTA technique for fault diagnosis (FD) in large-scale plants. Essentially, QTA is applied on the principal components rather than on the sensor data. The proposed approach is tested on the Tennessee Eastman (TE) process. The reduction in computational complexity in trend-extraction is about 40%. This reduction in computational complexity is expected to increase considerably for larger processes.