The automation of real-time process monitoring in the industry is an ongoing challenge. Chief among the objectives of monitoring are change point detection and the detection of faults in process variables and sensor measurements. In this paper, we propose a novel algorithm for change point and fault detection using Kantorovich Distance (KD), a metric induced from optimal mass transport theory. To evaluate the performance of the proposed method, we first evaluate the change point detection capability of the KD metric for data sampled from various probability distributions. Next, the fault detection performance of the KD metric is evaluated for three cases of faults-sustained bias, drift, and multiple intermittent biases - and contrasted against that of the traditional PCA-based metrics, Q and T2 statistics. The algorithm is tested on several case studies including a synthetic data, a simulated continuous stirred tank heater system and the benchmark Tennessee Eastman process. The results obtained showcase the superiority of the proposed algorithm over the conventional scheme. (C) 2019 Elsevier Ltd. All rights reserved.