Accurate knowledge of rotor and stator resistance variations in a squirrel-cage induction motor (SCIM) is crucial for the performance of sensorless control of SCIM over a wide range of speeds. This study seeks to address this issue with a single Extended Kalman Filter (EKF) based solution, which is also known to have accuracy limitations when a high number of parameters/states are estimated with a limited number of inputs. To this aim, different from the author's previous approach in operating several EKFs in an alternating manner (the so-called braided EKT), an 8th -order EKF is implemented in this study to test its performance for the simultaneous estimation of rotor and stator resistances with a single algorithm. Beyond the resistances, the EKE observer also estimates the load torque, rotor and stator fluxes and speed in the wide speed range (-n(max) < 0 < n(max)). The results indicate success with the accurate estimation of only one resistance at a time, and an acceptable performance in speed estimation only after considerable tuning of the covariance matrix coefficients, hence the superiority of the braided EKF approach to the 8th -order EKF in sensorless control of SCIMs with the available current and voltage inputs.