We consider two classification approaches. The metric-based approach induces the distance measure between objects and classifies new objects on the basis of their nearest neighbors in the training set. The rule-based approach extracts rules from the training set and uses them to classify new objects. In the paper we present a model that combines both approaches. In the combined model the notions of rule, rule minimality and rule consistency are generalized to metric-dependent form. An effective polynomial algorithm implementing the classification model based on minimal consistent rules has been proposed in [2]. We show that this algorithm preserves its properties in application to the metric-based rules. This allows us to combine this rule-based algorithm with the k nearest neighbor (k-nn) classification method. In the combined approach the rule-based algorithm takes the role of nearest neighbor voting model. The presented experiments with real data sets show that the combined classification model have the accuracy higher than single models.