Background: To evaluate QT-interval dynamics in patients and in drug safety analysis, beat-to-beat QT-interval measurements are increasingly used. However, interobserver differences, aberrant T-wave morphologies and changes in heart axis might hamper accurate QT-interval measurements. Objective: To develop and validate a QT-interval algorithm robust to heart axis orientation and T-wave morphology that can be applied on a beat-to-beat basis. Methods: Additionally to standard ECG leads, the root mean square (ECGRMS), standard deviation and vectorcardiogram were used. QRS-onset was defined from the ECGRMS. T-wave end was defined per individual lead and scalar ECG using an automated tangent method. A median of all T-wave ends was used as the general T-wave end per beat. Supine-standing tests of 73 patients with Long-QT syndrome (LQTS) and 54 controls were used because they have wide ranges of RR and QT-intervals as well as changes in T-wave morphology and heart axis orientation. For each subject, automatically estimated QT-intervals in three random complexes chosen from the low, middle and high RR range, were compared with manually measured QT-intervals by three observers. Results: After visual inspection of the randomly selected complexes, 21 complexes were excluded because of evident noise, too flat T-waves or premature ventricular beats. Bland-Altman analyses of automatically and manually determined QT-intervals showed a bias of <4ms and limits of agreement of ±25ms. Intra-class coefficient indicated excellent agreement (>0.9) between the algorithm and all observers individually as well as between the algorithm and the mean QT-interval of the observers. Conclusion: Our automated algorithm provides reliable beat-to-beat QT-interval assessment, robust to heart axis and T-wave morphology.