Through proper monitoring, problems can be identified and isolated well before the economics of the process are threatened. In contrast to most conventional methods, fouling can be detected when the heat exchanger operates in transient states. Statistical analysis is used to develop a fouling growth model of a heat exchanger subjected to fouling. The statistical analysis was considered for four different types of distributions out of which the lognormal distribution was found to be most suitable. Experiments were conducted with a single pass shell and tube heat exchanger with water both as the hot and cold fluids. The results show that the proposed tool is very effective in detecting critical fouling in a heat exchanger, which can be utilized for predicting the optimal maintenance schedule. Hence, the results of this work can find application in predicting the reduction in heat transfer efficiency due to fouling in heat exchangers that are in operation and assist the exchanger operators to plan cleaning schedules.