This research paper explores a variety of strategies for performing classification with missing feature values. The classification setting is particularly affected by the presence of missing feature values since most discriminative learning approaches including logistic regression, support vector machines, and neural networks have no natural ability to deal with missing input features. Our main interest is in classification methods that can both learn from data cases with missing features, and make predictions for data cases with missing features.