The tight production objectives and dynamically evolving conditions within industries necessitate the diligent monitoring and evaluation of control assets’ operational health. During the past decades, a lot of data-driven methods, using fundamentals of signal processing and process control or machine learning, have been developed to detect performance issues in control loops. One of those, machine learning based methods, has also become popular in recent years. However, the complexity of algorithms used, incapability of predicting more than “good” or “bad” or need for process excitations limits the practical use of these methods in large industrial scale. In this paper, an easy-to-use classifier has been developed which is based on only routine industrial closed-loop data available and common for many control systems. The developed classifier is able to classify the control loops as acceptable, aggressive tuning, sluggish tuning, stiction and external disturbance, which account for almost all common and major problems experienced in closed-loop PID controllers. The features calculated and given to the classifier are immensely easy-to-obtain metrics based on histograms of control error, auto-correlation function, and impulse response. The developed classifier has an 88% training and 85% test accuracy. The classifier has also been tested with a set of industrial loops assessed extensively by process control engineers and able to predict the true class of 88% of the loops, with a 3% false negative rate.