Objective: We present the improved method of recognition of sustained ventricular tachycardia (SVT) based on new filtering technique (FIR), extended signal-averaged electrocardiography (SAECG) description by 9 parameters and the application of support vector machine (SVM) classifier. Methods: The dataset consisted of 376 patients (100 patients with sustained ventricular tachycardia after myocardial infarction (MI) labelled as class SVT+, 176 patients without sustained ventricular tachycardia after MI and 77 healthy persons, 50% of data were left for validation. The analysis of SAECG was performed by 2 types of filtration: low pass four-pole IIR Butterworth filter and FIR filter with Kaiser window. We calculated 3 commonly used SAECG parameters: hfQRS (ms), RMS40 (µV), LAS<40 µV(ms) and 6 new parameters: LAS<25 µV(ms) - duration of the low amplitude <25µV signals at the end of QRS complex; RMS QRS(µV) – root mean square voltage of the filtered QRS complex; pRMS(µV) - root mean square voltage of the first 40ms of filtered QRS complex; pLAS(ms) - duration of the low amplitude <40µV signals in front of QRS complex; RMS t1(µV) - root mean square voltage of the last 10ms the filtered QRS complex; RMS t2(µV) - root mean square voltage of the last 20ms the filtered QRS complex. For the recognition of SVT+ class patients we used the SVM with the Gaussian kernel. Results: The results confirmed good generalization of obtained models. The recognition score (calculated as correct classification/total number of patients) of SVT+patients on data set containing 3 standard parameters (Butterworth filter) is 92.55%. The same score was obtained for data set containing 9 parameters (Butterworth filter). The best score (95.21%) was obtained for data set based on 9 parameters and FIR filter. Conclusion: Our approach improved risk stratification up to 95% based on SAECG due to the application of FIR filter, 6 new parameters and efficient statistical classifier, the support vector machine.