In this work, an artificial neural network (ANN) based framework for fault diagnosis in batch chemical plants is presented. The proposed FDS consists of an ANN structure supplemented with a knowledge based expert system (KBES) in a block-oriented configuration. The system combines the adaptive learning diagnostic procedure of the ANN and the transparent deep knowledge representation of the KBES. The information needed to implement the FDS includes a historical database of past batches, a hazard and operability (HAZOP) analysis and a model of the batch plant. The historical database that includes information related to normal and abnormal operating conditions is used to train the ANN structure. The deviations of the on-line measurements from a reference profile are processed by a multi-scale wavelet in order to determine the singularities of the transients and to reduce the dimensionality of the data. The processed signals are the inputs of an ANN. The ANNs outputs are the signals of the different suspected faults. The HAZOP analysis is useful to build the process deep knowledge base (KB) of the plant. This base relies on the knowledge of the operators and engineers about the process and allows the formulation of artificial intelligence algorithms. The case study corresponds to a batch reactor. The FDS performance is demonstrated through the simulation of different process faults. The FDS proposed is also compared with other approaches based on multi-way principal component analysis.