A novel neural-network model called GROUPSTRON is proposed to identify the k groups' elements from a data set. Based on both the divide-and-conquer principle and the coarse-and-fine competition, GROUPSTRON divides the identification process into k rounds and then sequentially identifies each group's elements from the data set. All the elements in the first group are larger than those in the second group and this relationship holds for the successive groups. The proof that GROUPSTRON converges to the correct state in every situation is also given. Moreover, the convergence rates of GROUPSTRON for three special data distributions are deduced. Finally, simulation results are given to demonstrate the effectiveness and design philosophy of GROUPSTRON.