Abstract Analytical instrument qualification (AIQ) is a prerequisite for any analytical method validation and thus must be considered as a vital basis of analytical data integrity and quality in pharmaceutical analysis. There is a well-established system of qualification phases—Design Qualification, Installation Qualification (IQ), Operational Qualification (OQ) and Performance Qualification (PQ). As HPLC systems are “off the shelf” equipment, Design Qualification may be disregarded here. IQ establishes that the instrument is received as designed and that it is properly installed. OQ is carried out modularly with the intention to ensure that the specific modules of the system and the whole system are operating according to the defined specifications. PQ as the last step of the initial qualification is supposed to ensure continued satisfactory performance of an instrument under actual running conditions over the anticipated working range during daily use. However, PQ is not a one time exercise, but is currently repeated regularly independently from routine use of the analytical system using standard reference test condition. But this approach, which is time consuming and expensive only provides a snapshot of system performance. As HPLC procedures generally require a system suitability test (SST) prior and/or after test, it might be far more reasonable and robust to use these SST data for a continuous PQ. The work presented here demonstrates that, under certain circumstances, satisfactory instrument performance assessment can be derived from system suitability tests and performance data from daily use as well. A generally accepted qualification list, consisting of only twelve critical parameters, was compiled in a first step. Some parameters such as injector or thermostatting accuracy were considered redundant while others were successfully incorporated in the proposed holistic approach. System suitability test data as well as OQ/PQ data were provided from different sources and evaluated. The promising results confirmed our concept of ongoing/continuous PQ as a major improvement in AIQ. This approach will not only help to reduce time and effort in the daily laboratory routine without losing data quality, but also avoid the critical re-evaluation of numerous analytical tests once a routine PQ fails.