In the chemical and biochemical industries, batch processes play an important role in producing high-quality and value-added specialty products. The variables measured in such processes often have periodic trajectories with varying batch duration and fixed operational stages. The measured value of a process variable provides only part of the information about process conditions, while the process monitoring and operation activities mostly rely on the trends and trajectories of these variables. It is necessary to construct a trend-monitoring statistic to detect an abnormality in a batch process variable trajectory to provide operation decision support and guarantee product quality. In this paper, a new trend cumulative sum (CUSUM) chart is introduced as a way to detect the deviation of batch process variable trends, which may be caused by abnormal operations. This method relies on the extraction of variable trend features as a combination of derivatives through a functional description of variable trajectories. This then leads to the construction of an individual trend CUSUM chart and a multiple-trend CUSUM chart. The multiple-trend CUSUM chart, the sum of the trend CUSUM charts of all process variables, aims to improve the monitoring efficiency by combining the trend deviations of all variables in a single chart. The potential of these novel univariate control charts is demonstrated using the batch manufacture of polypropylene (PP). When several simulated and actual batches are studied, the results show that the trend CUSUM chart is capable of capturing trend abnormalities. We also show, by comparison, that alarms can be triggered in the trend CUSUM chart before the measured process variables exceed the control limits of their Shewhart individuals charts, allowing for the corrections to be made at an early stage of an abnormal situation.