We examine Arctic sea-ice concentration (SIC) and sea-level pressure (SLP) data using principal oscillation pattern (POP) and neural network methods. The POP method extracts oscillating patterns from multivariate time series, each pattern being characterized by an oscillation period and a decay time. Predictions can be made for patterns whose decay time is comparable with the period. For both the SIC and SLP, however, the decay time are much shorter than the oscillation periods, and therefore the forecast skill is poor. A neural network is a model of the learning behaviors of a living neural system. Presented with training data, a neural network can learn the linear or nonlinear rules embedded in the data. We trained neural networks with sea-ice and sea-level pressure data, and estimated the forecast skill using a cross-validation technique. The neural networks did not exhibit forecast skill significantly better than that of persistence. We contrast the Arctic situation with previous studies in which POP and neural networks were successfully used to forecast EL Nino at lead times up to 6 months. Reasons for the lack of skill in both methods are discussed.