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The finger-cuff system CNAP (CNSystems Medizintechnik, Graz, Austria) allows non-invasive automated measurement of pulse pressure variation (PPVCNAP). We sought to validate the PPVCNAP-algorithm and investigate the agreement between PPVCNAP and arterial catheter-derived manually calculated pulse pressure variation (PPVINV). This was a prospective method comparison study in patients having neurosurgery. PPVINV was the reference method. We applied the PPVCNAP-algorithm to arterial catheter-derived blood pressure waveforms (PPVINV−CNAP) and to CNAP finger-cuff-derived blood pressure waveforms (PPVCNAP). To validate the PPVCNAP-algorithm, we compared PPVINV−CNAP to PPVINV. To investigate the clinical performance of PPVCNAP, we compared PPVCNAP to PPVINV. We used Bland–Altman analysis (absolute agreement), Deming regression, concordance, and Cohen's kappa (predictive agreement for three pulse pressure variation categories). We analyzed 360 measurements from 36 patients. The mean of the differences between PPVINV−CNAP and PPVINV was −0.1% (95% limits of agreement (95%-LoA) −2.5 to 2.3%). Deming regression showed a slope of 0.99 (95% confidence interval (95%-CI) 0.91 to 1.06) and intercept of −0.02 (95%-CI −0.52 to 0.47). The predictive agreement between PPVINV−CNAP and PPVINV was 92% and Cohen’s kappa was 0.79. The mean of the differences between PPVCNAP and PPVINV was −1.0% (95%-LoA−6.3 to 4.3%). Deming regression showed a slope of 0.85 (95%-CI 0.78 to 0.91) and intercept of 0.10 (95%-CI −0.34 to 0.55). The predictive agreement between PPVCNAP and PPVINV was 82% and Cohen’s kappa was 0.48. The PPVCNAP-algorithm reliably calculates pulse pressure variation compared to manual offline pulse pressure variation calculation when applied on the same arterial blood pressure waveform. The absolute and predictive agreement between PPVCNAP and PPVINV are moderate.