Summary: In this paper, we establish the limit of empirical spectral distributions of quaternion sample covariance matrices. Motivated by Z. Bai and J. W. Silverstein [Spectral analysis of large dimensional random matrices. 2nd ed. Dordrecht: Springer (2010; Zbl 1301.60002)] and V. A. Marchenko and L. A. Pastur [Math. USSR, Sb. 1, 457–483 (1968; Zbl 0162.22501)], we can extend the results of the real or complex sample covariance matrix to the quaternion case. Suppose \(\mathbf X_n = (x_{jk}^{(n)})_{p\times n}\) is a quaternion random matrix. For each \(n\), the entries \(\{x_{ij}^{(n)}\}\) are independent random quaternion variables with a common mean \(\mu \) and variance \(\sigma^2>0\). It is shown that the empirical spectral distribution of the quaternion sample covariance matrix \(\mathbf S_n=n^{-1}\mathbf X_n\mathbf X_n^*\) converges to the Marčenko-Pastur law as \(p\to \infty \), \(n\to \infty \) and \(p/n\to y\in (0,+\infty)\).