Pulse wave analysis (PWA) is commonly employed for the calculation of a multitude of physiological parameters related to cardiac pumping mechanics, arterial stiffness and peripheral vascular resistance. These parameters have been increasingly used in the last decades for the assessment of cardiovascular risk, which highlights their usefulness. In recent years new analytic strategies have focused on the spectral decomposition of pulse wave signals for the assessment of the dynamics of cardiac autonomic regulation, the best example being pulse rate variability (PRV) analysis. Spectral decomposition of pulse wave signals also provides insight into the mechanisms (central and local) regulating tissue perfusion through the assessment of the relative contribution of the different frequency intervals of the signals over time. Although there are striking differences between pulse wave signals from different anatomical regions, few studies have attempted to compare them on the basis of their frequency spectra. This study aimed to compare the frequency spectra of pulse wave signals obtained from the neck and fingertip regions and their respective underlying mechanisms. Ten young healthy subjects (23.4 ± 4.9 y.o.; 6 females, 4 males) participated in this study after giving informed consent. Pulse wave signals were obtained with photoplethysmography (PPG) sensors placed over a random common carotid artery and over the pulp of the second finger of the ipsilateral upper limb. PPG signals were recorded for 10 minutes while subjects were sitting upright and performing a simple postural modification – 5 min with both arms at heart level (phase I) and 5 min with one random arm placed 40 cm below heart level (phase II). The wavelet transform was used to decompose the raw PPG signals into their different frequency regions (high, low and very low frequency). The amplitude ratio of each frequency region was assessed over time and compared between phases of the protocol, as well as between signals. Nonparametric statistics were employed and a p<0.05 was adopted. Significant differences in the amplitude ratio of the frequency intervals were identified between signals, highlighting their different physiological origin. Significant differences were also detected between the different phases, with the finger PPG signals showing more pronounced changes during the postural change when compared to the carotid signals. Although preliminary, our results show that the wavelet transform is a useful tool to provide a spectral decomposition of pulse wave signals from different anatomical regions. In addition, spectral analysis provides useful insights into the physiological origins of these signals.