Good control handing comfort not only reduces fatigue but also improves efficiency. Aiming at the uncertainty and fuzziness of control handing comfort evaluation, this study builds up a control handing comfort evaluation model for standing posture based on Takagi-Sugeno Fuzzy Neural Network (T-S FNN). Training and testing data were collected during the experiment. Twenty adult test subjects were asked to complete 100 different operation tasks. Test subjects’ joint angle, foot pressure distribution, anthropometric dimensions, target position, and subjective comfort rating were collected during the experiment. The proposed model was trained using 90% of the data obtained from the experiment and was verified by the remaining 10% experiment data. It was then compared with the subjective comfort rating estimated by BP Neural Network. Results show that the proposed model had smaller root mean square error than BP Neural Network (1.2 vs. 4.5). Subsequently, 15 groups of different tasks were randomly selected to further test this model. Results show that the correlation coefficients between the value obtained by this model and the actual value, and those obtained by the Rapid Upper Limb Assessment (RULA) and the Ovako Working Posture Analysing System (OWAS) were 0.962 (P<0.01), 0.833 (P<0.01), and 0.694 (P<0.01), respectively. This study demonstrates that the proposed model is effective in estimating control handing comfort.