Transient disturbances are increasingly relevant in process industries which rely on electromechanical equipment. Existing data-driven methods for detecting transient disturbances assume a distinct amplitude or time-frequency component. This paper proposes a detection method which is more generic and handles any short-term deviation of a measurement from its overall trend, regardless of whether the trend incorporates features such as oscillations, noise or changes in operation level. The method is based on a nearest neighbors technique and builds a vector of anomaly indices which are high for the period with the transient disturbance. The paper includes analyses of the statistical significance of the threshold proposed and of the sensitivity of the parameters, and it also suggests a color map to visualize the detection results. The method is demonstrated on experimental and industrial case studies.