In this paper, we develop a novel framework, called Monitoring Vehicle Outliers based on a Clustering technique (MVOC), for monitoring vehicle outliers caused by complex vehicle states. The vehicle outlier monitoring is a method to continuously check the current vehicle conditions. Most of previous monitoring methods have conducted simple operations depending on uncomplicated analyses or expected lifetimes in regard to vehicle components. However, many serious vehicle outliers such as turning off during a drive result from the complex vehicle states influenced by correlated components. The proposed method monitors the current vehicle conditions based on not simple components like the previous methods but more complex and various vehicle states using a clustering technique. We perform vehicle data clustering and then analyze the generated clusters with information of vehicle outliers caused by complex correlations of vehicle components. Thus, we can learn vehicle information in more detail. To facilitate MVOC, we also propose related techniques such as sampling cluster data with representative attributes and deciding cluster characteristics on the basis of relations between vehicle data and states. Then, we demonstrate the performance of our approach in terms of monitoring vehicle outliers on the basis of real complex correlations between outliers and vehicle data through various experiments. Experimental results show that the proposed method can not only monitor the complex outliers by predicting their occurrence possibilities in advance but also outperform a standard technique. Moreover, we present statistical significance of the results through significance tests.