Optimal field weakening control of PMAC motor relies on accurate parameter knowledge. From modeling perspective, a good parameter set allows better correlation between the model and physical system. Errors in inputs (parameters) such as back-emf (Ke), phase resistance (R), cable resistance (Rc), phase Inductance (L) and voltage constant (Kv) have a coupled affect on the system outputs (measurements) such as torque, battery current and DC voltage; the relationship between inputs and outputs in not one to one. A single parameter change affects more than one output or vice-versa. For example, before the knee point, only Ke parameter affects the output torque but after the knee, all parameters influence the torque. Singular Value Decomposition (SVD) is an analysis technique that provides insight into the relationship between input and output of a system. It helps sort and weigh the parameters from most important to least important. The SVD outputs a U-matrix, V-matrix and S-matrix where U is a measurement matrix, V a parameter matrix and S a diagonal matrix of singular values that are weighting factors along the columns of V. SVD can also be used to determine the rank of the matrix which defines the number of independent columns or “directions”. A high S value means the respective column has a higher weighting and any error along that direction will strongly influence the measurements. The goal is to find the parameters that minimize the least square error between the model and the measurement and iterate until the error is below a specified threshold. This paper briefly presents the theory behind SVD, sensitivity studies for system response to small perturbations in parameters (and vice-versa in measurements) and applications to motor parameter estimation.