The cleaning of process plant has traditionally been an activity that has been carried out in open-loop mode, with confirmation of cleanliness achieved through off-line sample assessment. Such strategies have partly arisen as the depth of scientific understanding of the cleaning process has been limited. With deeper understanding through the tracking and prediction of cleaning progression, more sophisticated approaches can be adopted allowing the timely termination of cleaning operations. This paper discusses the component needs of the improved system. At its heart is the need to use appropriate measurement devices for the soil of interest to measure the current process condition and to derive predictive strategies to specify when to terminate cleaning. Results from a case study application on the cleaning of a toothpaste pilot plant demonstrate the concepts. The use of spectroscopic measurements is contrasted with more traditional measurements such as turbidity to track the cleaning profile. Improvement is not achieved simply through better measurement, algorithmic methods for measurement enhancement and forecasting to predict end point of cleaning are both necessary in order to achieve the termination of cleaning operations in a timely manner. The capability to perform both these tasks is considered using the experimental cleaning case study.