In this research, we have designed and implemented recursive least squares (RLS) algorithm in master slave tracking on Geomagic® Touch™ (Phantom Omni) haptic device. RLS algorithm enables us to achieve optimal tracking in a teleoperation system in which the system parameters vary with time and the noise is weakly non-stationary. In our previous work on teleoperation, we employed Widrow's least mean square algorithm instead of RLS algorithm and achieved satisfactorily high tracking accuracy. There, we employed instantaneous errors to update filter coefficients and hence slave positions. This study initiated with the idea that if we account all or some of the previous errors in updating filter coefficients and thus reducing current error, we might be probably able to achieve even higher tracking accuracy than that achieved with WLMS. Therefore, in order to understand this influence of older errors on tracking accuracy, we have applied RLS algorithm with forgetting factor. The use of forgetting factor in the least squares algorithm enables us to base our tracking on different weights of past errors that further helps us in understanding this influence at a broader level.