Designing and assembling automobiles is a complex task which has to be accomplished in ever shorter cycles. However, customers have increasing desires w. r. t. reliability, durability and comfort. In order to cope with these conflicting constraints it is indispensable to employ tools that greatly simplify the analysis of data that is collected during all car lifecycle stages. We will present methods for pattern discovery tasks for the development stage, the manufacturing and planning stage as well as for maintenance and aftercare. The first approach will reinterpret a Bayesian network to induce association rules which are then visualized to find interesting patterns. The second part will use Markov networks to model the interdependencies related to the planning task when assembling a vehicle. The last part deals with finding recurring patterns in time series used for adjusting simulation parameters.