In order to ensure high safety measures during perioperative stage, patients in intensive care units (ICUs), operating theatres or recovery rooms are continuously monitored. Although the technologies and tools in use are constantly improving, perioperative organ injury, as cause of singleor multiple organ failure, is still a major risk for patients in this stage. Current monitoring set-ups in the perioperative setting provide basic parameters like electrocardiogram (ECG) or oxygen saturation (SpO2). While these signals are important for general health status tracking, they lack to offer a predictive value regarding cardiovascular complications. Studies have shown that changes in the heart rate variability (HRV) correlate strongly with a high risk in multiple organ failure making this parameter an early marker for diagnosis. In an effort to introduce HRV to perioperative monitoring, a new tool was developed within the frame of this work. “Vital-signs REal-time Analysis for Clinical Translation”, VREACT, is an easy-to-use tool that allows for acquisition and continuous recording of high resolution biosignals from standard monitoring equipment. For the purpose of improving mortality risk analysis and prediction, our tool was enhanced by so called “modules”, which are derived biosignals that can use data from the monitors in order to easily introduce novel clinical parameters to perioperative monitoring. A semi online peak detection algorithm was implemented and applied on the ECG data in order to allow for HRV analysis. Afterwards different established methods for HRV inspection in the time domain as well as in the frequency domain were added as modules. VREACT also provides a sophisticated feature called “PatientViewer”, which enables real-time visualization of available modules. Finally, a modified version of VREACT called “VREACTquick” was developed, with the aim of handling long-term recordings without requiring user interaction. For the practical part, the performance of our tool was tested during real conditions in the General Hospital of Vienna. VREACT succeeded in recording over 15 patients from 3 different care units for over 2 hours. The PatientViewer managed to plot different modules correctly during 1 hour long tests for 5 randomly chosen patients. VREACTquick proved itself in an endurance test by collecting data from 2 different care units for over 3 days. For the future we expect our software to be used in various studies conducted in collaboration with the General Hospital of Vienna. Furthermore VREACT should constantly be improved by new modules and could also serve as a powerful acquisition tool in big data projects. In the course of this work one conference paper was submitted.