The anomaly detection problem for univariate or multivariate time series is a critical question in many practical applications as industrial processes control, biological measures, engine monitoring, supervision of all kinds of behavior. In this paper we propose an empirical approach to detect anomalies in the behavior of multivariate time series. The approach is based on the empirical estimation of conditional quantiles. The method is tested on artificial data and its effectiveness is proven in the real framework of aircraft-engines monitoring.