This work is devoted to application of data mining methods for monitoring of state of a solar thermal plant. The methods discussed are illustrated by example of a study performed for the DISS direct steam generation facility at the Plataforma Solar de Almeria (Spain). In order to deal with the problems of large dimensionality and high correlation among the data the methods of latent variables, principal component analysis and partial least squares, were applied. Results showed that normal and abnormal states during plant operation could be identified.