In many real world data applications, objects may have missing attributes. Conventional techniques used to classify this kind of data are represented in a feature space. However, usually they need imputation methods and/or changing the classifiers. In this paper, we propose two classification alternatives based on dissimilarities. These techniques promise to be appealing for solving the problem of classification of data with missing attributes. Results obtained with the two approaches outperform the results of the techniques based in the feature space. Besides, the proposed approaches have the advantage that they hardly require additional computations like imputations or classifier updating.