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Model-based fault detection becomes rather questionable if a supervised plant belongs to the class of systems with distributed parameters and significant delays. Two methods of fault detection have been developed for this class of plants, namely a method of functional (anisochronic) state observer and a modified internal model control scheme adopted for that purpose. ; Both these model schemes are employed to generate residuals, i.e. differences suitable to watch whether a malfunction of the control operation has occurred. Continuous evaluation of residuals is provided by means of a dynamic application of artificial neural networks (ANNs). ; This evaluation is carried out on the basis of prediction of time series evolution, where the accordance obtained between the prediction and measured outputs is used as a classification criterion. Implementation of both the methods is demonstrated on a laboratory-scale heat transfer set-up, making use of the Real-Time Matlab software.