The problem of sensor network design is to choose a set of variables that are to be measured in the process for satisfying various objectives related to estimability, data reconciliation, gross error detection, fault diagnosis, etc. Approaches to design base case sensor network as well as modify existing network by reallocating and upgrading sensors are available in the literature. In this work we extend the concept of reallocation and upgrade by developing an optimization based strategy to perform audit of a sensor network. Apart from identifying sensors to be reallocated and upgraded, the audit strategy also identifies sensors whose removal does not lead to a decrease in the performance of the sensor network. Such information can possibly be utilized in deciding what variables (or in what sequence) to be displayed to the process operator/engineer to accomplish a particular objective. In this work, we focus on fault diagnosis related performance criteria to illustrate the sensor network audit idea. In particular, we perform sensor network audit with objectives being: (i) ensuring observability of all faults, (ii) minimizing the unreliability of detection of faults (i.e. minimizing the probability of faults occurring and remaining undetected). The latter problem is in turn solved for two scenarios: when probability data and cause-effect models used for sensor network design are accurately known as well as for situations when there is uncertainty associated with such information. The resulting optimization problems are mixed integer linear programming in nature. The utility of the proposed approaches is demonstrated by applying them to the well known Tennessee Eastman process. (C) 2013 Elsevier Ltd. All rights reserved.