Profile measurements of structures with a high aspect ratio and subwavelength features (HARSW) can be achieved using transmission electron microscopy and tilted scanning electron microscopy. Although electron microscopy can provide accurate HARSW measurements, it is laborious and destructive. In this paper, nondestructive and labor-saving methods were proposed to measure the dimensions of HARSW structures. The optical reflection spectrum, along with an artificial neural network (ANN) model, was adopted for interpolation with the simulation database to retrieve the dimensions of HARSW structures. To generate the ANN model, the experimental and simulated reflection spectra were adopted as the input and output variables for the training data, respectively. This ANN model can learn the discrepancy between simulation and experimental reflections. The finite-difference time-domain method was also adopted to calculate the simulated reflection spectra of HARSW structures with various dimensions, which can be used as a database. Once the experimental reflection of a HARSW structure with unknown dimensions was obtained, the ANN model could generate a simulation-like reflection spectrum. Linear regression was used to determine the correlation coefficients of the simulation-like reflection spectra in the database. The accurate dimensions of HARSW structures can be determined using a higher correlation coefficient. This methodology can be a prominent method for the process monitoring of HARSW structures.