This chapter provides a technical overview of neural network approaches to time series prediction problems and discusses challenges associated with time series prediction and neural network solutions. Some of the important issues related to neural network time series modeling include incorporating temporal information, selecting input variables, and balancing model bias/variance trade-off. Three techniques — sensitivity based input selection and pruning, constructing committee prediction models using input feature grouping, and smoothing regularizer for recurrent neural networks — and their application to an economic time series prediction problem are presented in detail. This case study demonstrates how to tackle a time series prediction problem in the neural network paradigm.