Axial piston pump (APP) is extensively employed in a wide range of engineering applications, such as industrial hydraulic systems, and cooling units and etc., due to its high reliability and performance. However, despite its robustness, APP remains susceptible to various degradation mechanisms, such as wear, cavitation, and valve plate damage, which can result in reduced efficiency and operational reliability. Therefore, continuous condition monitoring is essential to ensure optimal pump performance and avoid unexpected system failures.This study introduces a methodology that integrates case drain flow rate evaluation with Principal Component Analysis (PCA) – to enable a comprehensive evaluation of the Pump Performance Index (PPI) – for a pump station comprising three industrial-scale APPs operating within a complex industrial hydraulic system.Monitoring the case drain flow is particularly advantageous for detecting wear-related degradation. A practical condition assessment strategy involves comparing the measured drain flow against predefined baseline values derived from theoretical nominal flow calculations. The most favorable conditions for monitoring the case drain flow are during dead-head operation (i.e., when the delivered flow rate approaches zero), at rated speed, and under maximum pressure. This methodology provides a robust foundation for early fault detection and performance-based maintenance in hydraulic systems.PCA facilitates the integration of multiple sensor-based parameters, including case drain flow rate, electrical power consumption, and temperature differentials, to evaluate the overall PPI.