Knowledge of cycle times is essential to design spouted beds. Although experiments for determining cycle times are time-consuming, there is hardly any mechanistic or empirical model for their prediction. Two types of artificial neural networks, namely, single- and multiple-output ones, have been developed to estimate particle cycle times in conical spouted beds. They provide satisfactory cycle time predictions under different configurations. A comparison of single-output neural networks with multiple-output ones proves that, although the former fit slightly better the experimental data, the latter provide a reasonable prediction of all cycle times, and therefore, only one neural network is sufficient for their prediction. The maximum cycle is the one of highest sensitivity to all the parameters analyzed.