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The concept of modeling the behavior of industrial processes is of great importance as it describes the possible states of equipment used in large industries, which once damaged, it usually costs both in time and money. In this paper, we propose a data-driven methodology for depicting three distinct states of a chemical reactor, (1) normal, (2) warning, (3) alert, by using machine learning techniques. A method for predicting the classification of data input, assists in prevention (early prognosis) of possible malfunctions. This method uses a combined linear trend analysis of the involved data which form the warning state of the reactor where the pre-incident conditions are fulfilled. Afterwards, it checks the possibility of the subsequent input to be classified in the alert state which is an indication that the reactor's active equipment, such as heating resistance, will start malfunctioning. The objective of the three main steps of the proposed methodology are: first, to reveal the number of clusters based on past data, second to train normal, warning and alert behavior-models and validate them and third to test them as well as verify the accuracy of linear trend analysis. The proposed methodology is based on the analysis of real data sets​ derived from the automation system of a chemical process located at CERTH/CPERI in order to identify real-life models for prognostic behavior for malfunction prevention. This approach is especially suitable for modern industrial systems that follow Industry 4.0 principles. The results reveal a robust modeling of the reactor's behavior with accuracy reaching 88,94%. • Novel data-driven architecture for behavior modeling of chemical reactors. • Data-driven prognosis framework for detection of electromechanical malfunctions. • Trained models based on real-life experimental data for high classification accuracy. • Online trend analysis and machine learning methods for 24/7 process monitoring. [ABSTRACT FROM AUTHOR]