In a pioneering work, [PERSON] et al. [26] introduced stochastic subspace identification (SSI) together with combined and deterministic models within a unified framework. Due to the direct use of the response data in identification process, the method was named data-driven stochastic subspace identification or SSI-DATA. SSI-DATA is a numerically robust algorithm that uses QR decomposition to project future data on the past subspace [27]. The proposed stochastic subspace method use Hankel block matrix of the output data to analyze system and extract the state space model. The proposed algorithm has been broadly approved and has been extensively applied for identification of the system parameters particularly in the SHM. [PERSON] and [PERSON] [28] introduced applied SSI in SHM. [PERSON] and [PERSON] [29] proposed using a fast SSI scheme to solve the least squares problems. [PERSON] and [PERSON] [30] defined an robust SSI algorithm by potential capability of rejecting uncertainty of systems. [PERSON] et al. [31] used a decentralized SSI algorithm for wireless sensor networks (WSNs). [PERSON] et al. [32] modified Hankel matrix by adding harmonic vectors to modify modal identification of harmonic excited structures with close natural frequencies. [PERSON] et al. [26] extended the concept of weight matrices as a basis for the column space of the extended observability matrix in subspace algorithm. Three different implementations for SSI were introduced that included: Unweighted principal component (UPC), principal component (PC), and canonical variate analysis (CVA). Though the most studies in the field of SSI use the fundamentals of the algorithm developed by [PERSON] et al. [26] in their analysis, the incorporated weight matrices in the identification process is not clearly demonstrated [33; 34; 35]. PC algorithm simplifies data by orthogonal transformation of observations to a set of independent variables termed as principal variables. The first principal components of this transformation have higher variance. CVA is a linear regression to quantify a relation between a normalized and expectation variable. UPC is the simplest of three SSI algorithms that considers unit weight matrices for both. Only a limited number of studies are available that compare the performance of weight matrices of PC, CVA, or UPC for system identification. However, the findings not only do not support each, other but also contradict each other in some cases. [PERSON] et al. [36] used SSI for modal testing of on-orbit space satellite appendage. The results show that PC approach was unable to estimate modal parameters. Identified mode shapes using UPC and CVA approaches match very well with simulation results. CVA had better performance for low order excited modes. [PERSON] et al. [37] stipulated that different weighting matrices yield similar results for modal parameter identification. However, [PERSON] [38] and [PERSON] et al. [39] indicated that SSI-UPC had superior performance compared to other weight matrices for damage detection of structures. [PERSON] et al. [40] demonstrated that the CVA is the most stable weighting option in experimental modal analysis whereas [PERSON] et al. [41] chosen the variant PC for system identification. Several researchers have discussed about using different parameters of SSI algorithms in identification robustness, accuracy, and computation time. However, limited number of research has comprehensively investigated the impact of weight factors in identified system using SSI algorithm. Moreover, the obtained results in these studies do not support superior performance in any specific SSI methodologies or contradictory results are reported in some cases. The present study aimed at evaluating the effect of using weight matrices on efficiency of the identified system parameters. The current paper investigates the theory behind the PC, UPC, and CVA algorithms and presents different examples to evaluate the weight matrices.