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This article presents differential relaying and condition monitoring of power transformer using minimal radial basis function neural network (MRBFNN). This type of RBF neural network uses a sequential learning procedure to determine the optimum number of neurons in the hidden layer without resorting to trial and error. This ANN-based scheme monitors operating conditions of the transformer and detects the fault and issues the trip signal in case of an internal fault. It has been realized through two different ANN structures using the minimal radial basis function (MRBF) learning algorithm. The proposed protection scheme has been evaluated using simulated data obtained through EMTP/ATP package. The results amply demonstrate the capabilities of the proposed fault detector (FD) and condition monitor (CM) in terms of accuracy and speed with respect to detection of fault, classification and pattern recognition of different events of power transformer. The number of training patterns and training time are drastically reduced and significant accuracy is achieved. [ABSTRACT FROM AUTHOR]