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The paper presents a fault diagnosis algorithm based on multidimensional probability density function (pdf) estimation which is suitable for stochastic nonlinear systems. The pdf of symptom vector is estimated with use of the Radial-Basis Function (RBF) and Hyperradial-Basis Function (HRBF) artificial neural networks (NN). The numerical example of diagnosis of a nonlinear system is presented. The influences of the NN parameters and learning on the algorithm performance are discussed.