Validation of analytical procedures is a vital aspect not just for regulatory purposes, but also for their efficient and reliable long-term application. In order to address the performance of the analytical procedure adequately, the analyst is responsible to identify the relevant parameters, to design the experimental validation studies accordingly and to define appropriate acceptance criteria. Establishing an acceptable analytical variability for the given application is of central importance as many other acceptance criteria can be derived from such a precision. Acceptable precision ranges for types of control tests and/or analytes can be obtained from validation, but also related activities such as transfer, control charts, or extracted from routine applications such as batch release or stability studies (data mining). Apart from compiling a database for general benchmarking, during such an information-building process, the reliability of the analytical variability of the specific procedure is more and more increased. This is important as a reliable target variability facilitates to detect or investigate atypical or out-of specification behaviour of analytical data in a routine application, thus improving the data quality and reliability. According to the life-cycle concept of validation, measures should be taken to maintain and control the validated status of analytical procedures during long-term routine application, such as monitoring relevant performance parameters (system suitability tests), control charts, etc. If the analytical system is demonstrated to be stable, i.e. under statistical control, a major variability contribution in LC originating from the standard preparation and analysis can be reduced. A concept of quantification by pre-determined calibration parameters instead of the classical approach of simultaneous calibration is described.