This paper proposes an item selection algorithm that can be used to neutralize the effect of time limits in computer adaptive testing. The method is based on a statistical model for the response-time distributions of the test takers on the items in the pool that is updated each time a new item has been administered. Predictions from the model are used as constraints in a 0-1 linear programming model for constrained adaptive testing that maximizes the accuracy of the ability estimator. The method is demonstrated empirically using an item pool from an operational, large-scale computer adaptive test. (Contains 4 figures and 14 references.) (Author/SLD)