This paper proposes the application of a pattern recognition–based technique to enhance the process of control rod position identification in pressurized water reactors (PWRs). The proposed technique employs a multivariant analysis technique, namely, principal component analysis (PCA) and clustering analysis (CA) to identify the position of the PWR control rod using its impact on the core radial thermal neutron flux along the axial track of motion. The results of these investigations have shown that the proposed technique successfully removed the limitation on the data size and any limitations imposed by outlier samples, extracted the noise, and provided near-instantaneous analytical and visual ways for position identification process with excellent generalization fitting and prediction efficiencies. In the context of this paper, multiple in-depth simulations are conducted to ascertain the efficiency of the proposed technique in identifying the control rod positions. These simulations have been conducted using a Westinghouse 2772-MW(thermal) PWR benchmark at 100% thermal power generation, where a three-dimensional TRITON FORTRAN-code has been utilized to simulate the radial thermal neutron flux of the PWR core. The PCA model is developed, tested, and generalized using the SIMCA software package. In addition, CA is also performed via the Minitab statistics software package in order to confirm the efficiency of the proposed technique.