Recent advances in the wind energy industry have stimulated the demand for automated condition monitoring mechanisms capable of mitigating the cost of operations and avoiding tremendous economic losses due to unplanned downtime. To this end, a wide range of normal behavior models have been developed to monitor wind turbine performance. However, since most models are tailored to a single target at a time, a separate model is required for each target and are thus deemed unwieldy and expensive to implement, particularly in large-scale wind farms. Therefore, this article advocates for a multitarget normal behavior model which is capable of monitoring multiple targets simultaneously. The proposed model is specifically based on heterogeneous stacked regressions, trained with normal data curated via kernel density estimation. The distinct targets are monitored through a control chart based on an exponentially weighted moving average chart and a change-point detection (CPD) method via binary segmentation for wind turbine suboptimal performance detection. Extensive experiments based on real-world wind farm data are carried out and the results are compared with state-of-the-art methods. The attained results indicate that the proposed model is highly effective in not only reducing the number of models required for monitoring wind turbines, but also in improving model accuracy significantly.