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In the paper the new approach to create artificial neural networks (ANNs) is proposed. ANN's are inspired by natural neural networks (NNNs) that receive data in time still tuning themselves. In opposite to them ANNs usually work on the training data (TD) acquired in the past and are totally available at the beginning of the adaptation process. Because of this the adaptation methods of the ANNs can be sometimes more effective than the natural training process observed in the NNNs. This paper presents the ability of ANNs to adapt more effectively than NNNs do if only all TD are known before the beginning of the adaptation process. The design and adaptation process of the proposed ANNs is divided into two stages. First, analyze or examining the set of TD. Second, the construction of neural network topology and weights computation. In the paper, two kinds of ANNs which use the proposed construction strategy are presented. The first kind of network is used for classification tasks and the second kind for feature extraction.