We describe the complexity of an approach to fill missing values in decision trees during classification. This approach is derived from the ordered attribute trees method which builds a decision tree for each attribute and uses these trees to fill the missing attribute values. Both our approach and theirs are based on the Mutual Information between the attributes and the class. Our method takes into account the dependence between attributes by using Mutual Information. The result of the classification process is a probability distribution instead of a single class. In this paper, we explain our classification algorithm. We then calculate the complexity of constructing the attribute trees and the complexity of classifying a new instance with missing values using our classification algorithm.