For practical industrial processes, detection and isolation of a sensor precision degradation fault is of vital importance. In comparison with other sensor fault types such as complete failure and mean shift, the precision degradation fault is usually more difficult to detect and further to isolate. This paper proposes a new fault detection algorithm for sensor precision degradation using Kullback-Leibler divergence (KLD). The KLD is employed to quantify the dissimilarity between probability densities of each reference score and the actual one within the principal component analysis (PCA) framework. Under the assumption of Gaussian-distributed data, KLD for each score reduces to a measure of the variance difference, which is proven effective in detecting sensor precision degradation. Control limit of the KLD is established based on historical data, and onlinefault detection is implemented by adopting a sliding window. The fault isolation issue is also discussed, which aims to determine the faulty sensor with precision degradation. This task may be achieved by checking the PCA loading matrix and matching the fault detection result. Case studies on a synthetic numerical example and the continuous stirred tank reactor (CSTR) process are carried out to demonstrate the effectiveness of the proposed method, in comparison with conventional approaches.