The principal objective of this work is the identification and the location of sensors on a complex chemical plant needed for online process situation monitoring, fault detection and diagnosis of malfunctions. This identification is based on the use of a classification technique and a measure of the quantity of information provided by the process variables, the entropy. Any classification method providing an interpretable description of the classes describing the process situations can be applied. In this work, the LAMDA (Learning Algorithm for Multivariate Data Analysis) classification method was employed for the design of the support tool. LAMDA combines Fuzzy Logic concepts, such as the adequacy of an element to a class, and the neural model representation. It allows, without changing of algorithm, to carry out classifications using a supervised (directed) or unsupervised (automatic) learning stage. The illustration of such a methodology is shown on a classical chemical plant: the propylene glycol production plant. This chemical process is composed of a mixer, a chemical reactor (CSTR) and a rectification column. This plant has been designed and simulated (dynamic simulation) using the well-known HYSYS simulation package. This simulation model has been used to generate scenarios of the various faults and malfunctions generally encountered in this type of plant. In particular, faults affecting the production quality have been simulated. After a short presentation of the most popular classification methods and the Entropy concept, the steps for the development of the proposed support tool are explained. This methodology is then applied to the example of the propylene glycol production plant. The present results highlight the contribution of both the methodology to select the "right" sensors and the classification technique to the design of a behavioral model used for monitoring and fault detection. (c) 2007 Published by Elsevier B.V.