This paper explains the development of a delineation algorithm for ECG signals and ST segment classification, based on both wavelet transform and support vector machine (SVM), taking advantage of their specific characteristics. The discrete transform with the mother wavelet Daubechies 4 was used to make the pre-processing, signal filtering and QRS complex detection. The selection of the set of coefficients was made according to the energy of each wavelet's decomposition level. The continuous transform was implemented for T and P wave detection. The detection of the onsets and offsets of each of these waves was evaluated using a combination of both types of wavelet transform, allowing the identification of the characteristic components in ECG signal. Samples of different kinds of diseases contained in QT Database were used for the validation. For the QRS complex it was found a sensivility Se=99,8% and a positive predictivity of P+=99,8%; and for P, QRS and T delineation values of sensibility and positive predictivity over 96% were found applied on different morphologies and different leads. For the ST classification with the SVM it was used different kinds of characteristic vectors, it was found a highest sensitivity of 98.8% and a average near to 80%.