Artifacts are noises introduced to an EEG signal by patient's movements and sources of electric field outside the patient's body. The artifacts impede a doctor?s expertise and an automatic analysis of the signal. The most common and characteristic kind of artifacts are blinking artifacts. The aim of this work was to create a computer program finding blinking artifacts in the EEG signal. There is no a set of rules determining if the fragment of the signal does contain a blinking artifact, or does not. Hence, neural networks were chosen as a classification tool. The input to the network is not a raw sampled signal, but different coefficients computed for a window of one second of the signal. The window moves by 0.25 second of signal. To create training set for the network, one analysed 500 MB of EEG signal, from which one chose over 27000 the most representative windows, containing different kind of artifacts, pathological and proper waves. Three classification algorithms were tested: k-neighbours, RBF networks and back propagation networks. The lowest classification error for the test set was obtained for the back propagation network with 5 hidden units. The program was tested on the EEG signal and was highly evaluated by a domain expert.