Historical data collected from processes are readily available. This paper looks at recent advances in the use of data-driven models built from such historical data for monitoring, fault diagnosis, optimization and control. Latent variable models are used because they provide reduced dimensional models for high dimensional processes. They also provide unique, interpretable and causal models, all of which are necessary for the diagnosis, control and optimization of any process. Multivariate latent variable monitoring and fault diagnosis methods are reviewed and contrasted with classical fault detection and diagnosis approaches. The integration of monitoring and diagnosis techniques by using an adaptive agent-based framework is outlined and its use for fault-tolerant control is compared with alternative fault-tolerant control frameworks. The concept of optimizing and controlling high dimensional systems by performing optimizations in the low dimensional latent variable spaces is presented and illustrated by means of several industrial examples. (C) 2012 Elsevier Ltd. All rights reserved.