We introduce a vision transformer (ViT)-based deep joint source and channel coding (DeepJSCC) scheme for wireless image transmission over multiple-input multiple-output (MIMO) channels, called DeepJSCC-MIMO. We employ DeepJSCC-MIMO in both open-loop and closed-loop MIMO systems. The novel DeepJSCC-MIMO architecture surpasses the classical separation-based benchmarks, while exhibiting robustness to channel estimation errors and flexibility in adapting to diverse channel conditions and antenna configurations without requiring retraining. Specifically, by harnessing the self-attention mechanism of the ViT, DeepJSCC-MIMO intelligently learns feature mapping and power allocation strategies tailored to the unique characteristics of the source image and prevailing channel conditions. Extensive numerical experiments validate the significant improvements in both distortion quality and perceptual quality achieved by DeepJSCC-MIMO for both open-loop and closed-loop MIMO systems across a wide range of scenarios. Moreover, DeepJSCC-MIMO exhibits robustness to varying channel conditions, channel estimation errors, and different antenna numbers, making it an appealing technology for emerging semantic communication systems.