An effective development strategy is needed to implement short development times for mechatronic systems, which themselves are becoming increasingly more complex. Besides the ever-increasing use of advanced computer-based development tools, special attention must be paid to the validation of the functional behavior of the prototype during all phases of the development process.Using the example of systems for powertrain automation, this paper depicts the resulting advantages arising from the long-time observation of time-based values of open- and closed-loop controlled systems. The method of long-time observation introduced here is based on the acquisition and analysis of information in all phases of the development process.Where numerous vehicles are already in the hands of selected customers during fleet tests, these late phases in particular are covered thoroughly. It should be noted that newly developed systems are normally unobserved or only subjectively evaluated by the driver.New aspects arise regarding the evaluation of the control system behaviour, its quality and its reliability, as the system is in use in real-word operating conditions over a broad range of different and partially extreme operating conditions, enabling the system designer to reproduce and observe all desired and undesired effects. In particular, hard-to-reproduce effects, such as the detection of sporadic faults in specific operating situations or unpredicted interactions with particular drivers, can be proven with long-time data acquisition and observation strategies.New aspects also arise in the fact that the method introduced here comprises in particular the synchronous/parallel acquisition and analysis of internal and external data of the electronic control system (ECU). The ability to compare ECU-internal and external data is an essential contribution for control strategy optimization and validation, not only for the confirmation of diagnosis functions, but also for the check-up and evaluation of calibration values.An essential prerequisite is an unobtrusive data acquisition system, which can utilize data from vehicle data-buses and other digital interfaces, such as for diagnosis and calibration purposes, as well as from analogue sensors. As the data produced here contains a large amount of real-world elements produced by the customers themselves, it can also be used for investigations under statistical aspects: The elaboration of suited data logistics leads finally to a database, which can be used for a broad range of statistically ensured investigations and analysis tasks. Examples given here include user and load profiles, the driving behavior of specific user groups and the estimation of component characteristics.The chosen method contributes directly to the inclusion of the customer as the driver, demanded, for example, in Quality Function Deployment during customer orientated product development.