This thesis focuses on the data mining process, namely the process of distilling information from raw data. The innovative techniques of data pre-processing, the statistical methods to properly treat and represent the data and the algorithms for supervised or unsupervised problems are described from a theoretical point of view and they are applied to the real business case of obtaining a more precise and objective estimate of truck’ s score at buy back based on the insights gathered from the telematic data. Indeed, CNHi has provided its trucks with two applications, UTP and P&CM, that receive from sensors information relative to internal and external parameters, how the vehicle is driven and if some faults occur.