The global receptive field plays a critical role in visual object tracking. In most popular tracking paradigms, we find that the local receptive field introduced by the convolutional neural network prevents the tracker from focusing on the long-range dependency. Although the Vision transformer brings the global receptive field in downstream tasks, its computational burden remains unaffordable. In this paper, we present a simple yet effective Multilayer Perceptron-based Tracking (MLPT), including the global receptive field. The MLPT contains three Components: Feature Correlation (FC) module, Global Information Encoder (GIE) module and Corner Head(CH). Firstly, the FC module is proposed to effectively converge the template and search region features for generating delicate features. Secondly, the GIE is designed to integrate the channel and spatial-information dealed with channel-encoding the token-encoding, separately. Specially, the same kernel is utilized for token-encoding in all channels so that our model has the global receptive field. Then, the CH is applied to establish a simple flexible way via computing the box corners coordinates for tracking. Finally, the MLPT, to our knowledge, is the first baseline of MLP-based architecture for object tracking. Extensive experiments are conducted on four challenging datasets, including GOT-10K, LaSOT, UAV123, and TrackingNet. The results show that the proposed method achieves state-of-the-art performance.