Considering the expected air traffic growth, the innovation and development of new tools able to allow a more efficient and safer way of managing aircraft operations is necessary to achieve future expectations. In this context, it is important to be capable of accurately predict aircraft trajectories to ensure efficient aircraft operations (e.g., planning and dispatching of flights, in-flight trajectory prediction, etc.) as well as to make more robust the Air Traffic Management (ATM) system (which includes ground based systems for ATC, predicting demand in ATC sectors, etc). The way of predicting them is based on aircraft performance models (APM), i.e., sets of equations that allow to model the aircraft performance according to some specific coefficients that depend on the aircraft carrying out the flight. Therefore, the predicted trajectories accuracy will depend directly on the aircraft performance model used. If the APM does not reflect the reality, the predicted trajectories will not be accurate enough. Moreover, since these trajectories will not be longer optimal according to the real performance model, the performance in cost-efficiency and the environmental impact of aircraft operations will be decreased. Then, it is required the use of aircraft performance models as realistic as possible. The objective of this Master Thesis is the design of an algorithm capable to estimate the coefficients of the functions describing the aircraft performance model considered, which will be the Base of Aircraft Data (BADA), from Quick Access Recorder (QAR) flight data. The idea is to obtain the real coefficients values of each specific tail number aircraft to accurately model their performance for trajectory prediction purposes. To perform the coefficients' estimation, real flight data has been used, consisting in a set of trajectories carried out by more than 30 different tail number aircrafts and obtained from their respective QAR. Among these aircrafts, only one (i.e, its trajectories) will be selected to design the estimation algorithm, which consist in a nonlinear least squares optimization problem. The output will be the coefficients value that better fits to the QAR data used, i.e, that minimize the difference between the real trajectories and the generated predicted trajectories as a result of applying the estimated coefficient's value to the BADA model equations. The results show a good estimation accuracy for the four aircrafts analysed, obtaining a very low prediction error in all cases, which proves the reliability of the algorithm and the necessity of tailoring the existing performance models (in this case BADA).