In this paper the author investigated the implementation of the convex optimization method in the area of the estimation of model parameters from experimental data. The investigation focused on the identification of processes within the technical environment such as: the liquid flow process, the mechanical vibrating process and the electric arc discharge. The theoretical support related to the convex optimization algorithm is emphasized in the first section of the paper. The preconditions for the implementation of the algorithm within the system identification context are also presented. In the third and the fourth sections, the main analysis is made. The mathematical models of the processes under investigation and the software implementations are depicted. The results showed that for an input signal, equivalent with the Dirac impulse, the disciplined convex optimization algorithm provide consisted estimate of the process under investigation which is similar to the results from the classical least-squares identification algorithm. These results are the basis for further investigations on the implementation of the convex optimization algorithm for system identification.