Detecting change in sensor measurements is essential for maintaining product quality and ensuring efficiency in manufacturing processes. Traditionally, statistical methods such as control charts are used to detect changes by comparing new sensor measurements with historical data. However, in high-dimensional, short-run (HDSR) settings, where there are many sensors and only limited or no historical observations, change detection becomes challenging and sometimes even impossible. HDSR processes are mostly present in specialized industries where errors can be costly, such as: semiconductors, aerospace or shipping. Previous research highlights several control charts to address HDSR processes and also demonstrated that grouping sensors can improve change detection. Finding groups of sensors was done by incorporating expert knowledge or by combining similar sensor data to increase sample size. In this research, a novel procedure for finding groups of sensors is proposed, by using an algorithm that automatically groups sensors based on the maximization of the probability of detection. The procedure and three state-of-the-art alternatives are applied to a case study involving a semiconductor manufacturing process of a new electron optical module. The results reveal that the proposed procedure finds groups of sensors that reflect sensor covariance and process knowledge. Furthermore, it is shown that the probability of detecting persistent mean shifts is improved compared to the three alternative control charts. Specifically, the proposed procedure had faster detection of shifts and also a higher POD for small magnitude shifts. Areas for future research could be the extension of the proposed procedure to a Bayesian framework.