A multi-input and multi-outpul support vector regression algorithm is introduced to predict the real-time object surface temperature of casting slab in secondary cooling zone by using the given object teinperaiure schedule calculated by metallurgical technicians. Test results of casting 200 mm x 1 534 mm slab of steel 16Mn show that with same trainingspecimen number

the training time by support vector regression algorithm is 3. 2 s with error of predict object temperature ± 1 ℃

while the training time by BP ( back propagation) neural networks algorithm is 23. 5 s with error of predict object temperature ± 2 ℃

and the multi-input and output support vector regression aigorithm is better than BP neural networks algorithm to alter the object surface temperature of slab in real time according to the variety of process

therefore it is available to dynamic-control the casting process and increase the quality of slab.