This article deals with the implementation of fiber-optic Bragg Grating Sensors signal processing methods for the detection of respiration rate, pulse rate, and body temperature. The sensed signals are influenced by a variety of interferences (motion artifact, environmental noise, etc.). Clinically relevant information is only available at certain frequencies, while the utilized optical sensor is able to cover relatively broad spectrum range. For real-world medical applications, the desired signal needs to be separated from the noise, which can often be other clinical information. This article introduces a virtual instrument for the extraction of clinically relevant information, such as respiration and heart rate, and body temperature. Frequency-selective filters were implemented in the proposed application. The functionality of the application was tested on real data using the FBGUARD and LabVIEW evaluation unit. The results were verified with commercially available devices and also statistically processed. Experimental results have shown that Fiber-Optic Bragg Grating Sensor signal processing is a key aspect of a successful incorporation of these sensors into clinical practice.