We have developed a fuzzy logic-based algorithm to qualify the reliability of heart rate (HR) and respiratory rate (RR) vital-sign time-series data by assigning a confidence level to the data points while they are measured as a continuous data stream. The algorithm's membership functions are derived from physiology-based performance limits and mass-assignment-based data-driven characteristics of the signals. The assigned confidence levels are based on the reliability of each HR and RR measurement as well as the relationship between them. The algorithm was tested on HR and RR data collected from subjects undertaking a range of physical activities, and it showed acceptable performance in detecting four types of faults that result in low-confidence data points (receiver operating characteristic areas under the curve ranged from 0.67 (SD 0.04) to 0.83 (SD 0.03), mean and standard deviation (SD) over all faults). The algorithm is sensitive to noise in the raw HR and RR data and will flag many data points as low confidence if the data are noisy; prior processing of the data to reduce noise allows identification of only the most substantial faults. Depending on how HR and RR data are processed, the algorithm can be applied as a tool to evaluate sensor performance or to qualify HR and RR time-series data in terms of their reliability before use in automated decision-assist systems.