Abstract: The present disclosure discloses method and control system for detecting condition of plurality of process equipment in industrial plant. The proposed methodology implements machine learning techniques for detecting the fault. The machine learning techniques include supervised learning and unsupervised learning. Real-time values of plurality of parameters associated with each of the plurality of process equipment along with weight matrix and threshold attribute is used in the unsupervised learning to detect the condition. The weight matrix associated with each of the plurality of process equipment is generated using the supervised learning. Plurality of historic values of the plurality of parameters relating to the corresponding process equipment are analysed in the supervised learning to generate the weight matrix for the corresponding process equipment.