In this study, we present the application of a hybrid neuro-fuzzy system for the prediction of cotton rotor spun yarn strength from cotton fiber properties. The proposed system possesses the advantages of both artificial neural networks and fuzzy logic, and is thus more intelligent. HVI (high volume instrument) and Uster AFIS (advanced fiber information system) fiber test results are used to train the neuro-fuzzy inference system. We also study the degree of impact of each fiber property on the rotor spun yarn strength. Fiber strength, upper half mean length, length uniformity and yarn count have a positive impact whereas micronaire, yellowness and short fiber content have a negative impact on rotor spun yarn strength.