Conventional method of error compensation for truck scales is fussy and the weighing results are not precise, so an error compensation method based on multiple radial basis function neural networks (RBFNN) was proposed. The sub-RBFNNs were confirmed according to the maximum permissible error of scale's different verified weighing capacities. The compensation models were established and then the algorithm of training RBFNN was presented. Sub-RBFNNs were combined and one of them was chosen to fully compensate weighing error of every capacity by adaptive network, and then optimum compensation of total weighing range was obtained. Simulation experiments show that the sub-RBFNN's size of this proposed method is smaller and the errors are less than those of the method that compensates the error of total weighing range with single RBFNN.