Unscheduled maintenance is a large cost driver for airlines, but condition monitoring and prognosis can reduce the number of unscheduled maintenance actions. The paper shows condition monitoring can be introduced into most system by adopting a data-driven approach and using existing data sources. The goal is to forecast the remaining useful life (RUL) of a system based on various sensor inputs. We use decision trees to learn the characteristics of a system. The data for the decision tree training and classification are processed by a generic parametric signal analysis. To obtain the best classification results for the decision tree, the parameters are optimized by a genetic algorithm. A forest of three different decision trees with different signal analysis parameters is used as classifier. The proposed method is validated with data from an A320 aircraft from ETIHAD Airways. Validation shows condition monitoring can classify the sample data into ten predetermined categories, representing the total useful life (TUL) in 10 percent steps. This is used to predict the RUL. There are 350 false classifications out of 850 samples. Noise reduction reduces the outliers to nearly zero, making it possible to correctly predict condition. It is also possible to use the classification output to detect a maintenance action in the validation data.