  We present a method for resolving the combinatorial issues in the \ttbar
lepton+jets events occurring at the Tevatron collider. By incorporating
multiple information into an artificial neural network, we introduce a novel
event reconstruction method for such events. We find that this method
significantly reduces the number of combinatorial ambiguities. Compared to the
classical reconstruction method, our method provides significantly higher
purity with same efficiency. We illustrate the reconstructed observables for
the realistic top-quark mass and the forward-backward asymmetry measurements. A
Monte Carlo study shows that our method provides meaningful improvements in the
top-quark measurements using same amount of data as other methods.
