This work investigates a classification technique based on centrality properties of complex networks. Different from traditional classifiers which consider only the physical features of the data (e.g., similarity or distribution), the technique under study also considers structural and topological features of the networked data. In previous studies the technique takes into account the individual importance of each input data in the classification of new instances by adopting the well-known pagerank measure, while other relevant centrality measures were not even considered. In this paper we cover such a lacuna by analyzing a total of five relevant centrality measures from the literature, namely: pagerank, betweenness, closeness, degree and shortest path length. Such measures had their bias evaluated over several real-world data sets in terms of predictive capability and robustness. The results showed that pagerank and degree often achieved the best results and also outperformed statistically all other measures in terms of predictive robustness. In a few words, these findings may support both the understanding and appropriate selection of complex network measures for machine learning tasks.