Background: Infants with congenital heart disease who have insufficient systemic or pulmonary blood flow require a connection between the systemic and pulmonary vasculature. There are four types of shunts: right ventricle to pulmonary artery (Sano), Blalock-TaussigThomas (BTTS), central, and ductal arterial stent (DAS). They have a high incidence of mortality and unplanned interventions. Machine learning algorithms evaluate a shunt murmur recorded on a digital stethoscope to differentiate the audio features of various shunts. Methods: A 3M Littman Electronic Stethoscope acquired phonocardiogram (PCG) data from infants with shunt placement on post-operative days 0, 1, 2, 7, 14, discharge and day of shunt take-down, as well as, when imaging (echo, CT, catheterization) was performed. Patient demographics and clinical outcomes noted. A wavelet decomposition algorithm denoised the PCG data with a bandpass filter ranging from 20 to 2000 Hz and then segmented into S1 and S2. One hundred and fifteen audio features, comprising temporal, spectral, statistical, and bandpower domains were extracted from the waveforms. A statistical algorithm including ANOVA, Kruskal Wallis and pairwise comparison tests detected 33 total features showing a statistically significant difference in means (alpha = 0.05). Results: Thirteen subjects (BTTS (n=3), DAS (n=2), and Sano (n=8)) who had second stage palliation were analyzed using the two most recent recordings prior to shunt takedown. Principal component analysis (Figure 1) and tdistributed stochastic neighbor embedding (Figure 2) visualization techniques indicate a separation among shunt types. Conclusion: Audio signatures could exist based on shunt type characteristics and may lead to a novel non-invasive tool to monitor shunt health. Figure 1. Principal Component Analysis (PCA) indicating a separation in BTTS, Sano, and PDA shunt types from selected recordings. Figure 2. t-distributed stochastic neighbor embedding (tSNE) indicating a separation in BTTS, Sano, and PDA shunt types from selected recordings.