The present work evaluates the performance of different optimisation techniques on a parameter identification problem of aeronautical interest. In particular, the focus is on the classical Least Square (LS) and Maximum Likelihood (ML) methods and on the CMAES (Covariance Matrix Adaptation Evolution Strategy), DE (Differential Evolution), GA (Genetic Algorithm) and PSO (Particle Swarm Optimisation) Meta-Heuristic methods. The test problem is the reconstruction from flight test data of the aerodynamic parameters of an external store jettisoned from a helicopter. Different initial conditions and the presence of measurement noise are considered. This case is representative of a class of problems of difficult solution because of nonlinearity, ill-conditioning, multidimensionality, non separability, and fitness function dispersion. Only reference algorithm implementations found in literature are used. The performance of each algorithm are defined in terms of fitness function value, sum of absolute errors of the estimated coefficients, computational time and number of function evaluations. The results show the efficiency of CMAES in finding the best estimates with the least computational cost. Moreover, tests reveal that traditional methods depend heavily on problem characteristics and loose accuracy at the increase of the number of unknowns.