In recent years, techniques of using system disturbance data to validate generator models have been widely discussed. Dynamic model validation and calibration is becoming one of the important applications to smart grid initiative. As this kind of technique is utilized to validate generator model, procedures of data screening and reprocessing are essential because raw data obtained from measurement is not always satisfactory. Regarding model parameter calibration, system model is usually quite complicated with various parameters interacting with each other. Artificial intelligent tool is the prior option to save the laborious tuning process and enhance the parameter accuracy. This paper presents a guideline to validate and calibrate parameters of generating units using the record data from phasor measurement unit. Associated procedures for signal filtering on the measurement data, key parameters screening, intelligent search of model parameters, and cross check of legitimate parameters will be discussed in detail. Finally, two historical disturbance cases that happened in the Taiwan power (Taipower) system are applied in accordance with the proposed guideline to demonstrate its effectiveness on generator parameter validation and calibration.