We meet with time series problem in all areas of life. Engineering areas are not expection, where the most of measured data are time based. In my thesis I would like to demonstrate, that the neural networks can be effective and good solutions for this problems. I show firstly the general theorethical of neural networks, than I choose the software environment. In the second part of my thesis I introduct three problem, where I show the neural netwoks in practise. Firstly I present a classification problem, what I solve with deep neural network. In this section I show, how can we use the convolution neural networks on audio signals. The second task is the prediction of pollution in Bejing. I write first about dynamic neural networks, then I show my sollution and I talk the setup of the deep neural network (LSTM). In the last task I present an anomaly detection neural network on ECG signals, and I briefly present what other anomaly detections algorithm still exits, then I show my solution capabilities. Finally I sum my experiences and results.