Low systemic vascular resistance (SVR) can be a useful indicator for early diagnosis of critical pathophysiological conditions such as sepsis, and the ability to identify low SVR from simple and noninvasive physiological signals is of immense clinical value. In this study, an SVR classification system is presented to recognize the occurrence of low SVR, among a heterogenous group of patients (N = 48), based on the use of routine cardiovascular measurements and features extracted from the finger photoplethysmogram (PPG) as inputs to a quadratic discriminant classifier. An exhaustive feature search was performed to identify a near optimum feature subset. Cohen's kappa coefficient (κ) was used as a performance measure to compare candidate feature sets. The classifier using the following combination of features performed best (κ = 0.56, sensitivity = 96.30%, positive predictivity = 92.31%): normalized low-frequency power (LFNU) derived from PPG, ratio of low-frequency power to high-frequency power (LF/HF) of the PPG variability signal, and the ratio of mean arterial pressure to heart rate (MAP/HR). Classifiers that used either LF(NU) (κ = 0.43), LF/HF (κ = 0.37) or MAP/HR (κ = 0.43) alone showed inferior performance. Discrimination of patients with and without low SVR can be achieved with reasonable accuracy using multiple features derived from the PPG combined with routine cardiovascular measurements.