Electrocardiography (ECG) signals and the information obtained from the analysis of these signals constitute the main source of information in determination of numerous cardiovascular system disease as well as in utility analysis of methods to be used for treatment of these diseases. In this study, ECG signals measured from living being are analyzed in computer environment in two parts namely filtering and feature extraction. In filtering phase, low frequency fluctuation on the baseline of the signal are straightened first using wavelet transform based filtering and then electric network noise are removed by designing a low pass butterworth filter. In feature extraction phase, timings of ECG characteristic waves (P, QRS complex, T) are determined using wavelet decomposition method. Obtained results show %95 or over success in determination of P, QRS and T timings. Measured ECG data used in this study are provided from Mersin University Medicine Faculty Biophysics Department and MIT physio database (www.phsionet.org).