Qualitative trend analysis (QTA) of sensor data is a useful tool for process monitoring, fault diagnosis and data mining. However, because of the varying background noise characteristics and different scales of sensor trends, automated and reliable trend extraction remains a challenge for trend-based analysis systems. In this paper, several new polynomial fit-based trend extraction algorithms are first developed, which determine the parameters automatically in the hypothesis testing framework. An existing trend analysis method developed by Dash et al. (2004) is then modified and added to the abovementioned trend extraction algorithms, which form a complete solution for QTA. The performance comparison of these algorithms is made on a set of simulated data and Tennessee Eastman process data based on several metrics. (C) 2015 Elsevier Ltd. All rights reserved.