In this paper we consider sample with missing data and sample without missing data, that comes from multivariate normal distribution with parameters mean vector and covariance matrix. No matter the shape of the data we can estimate parameters with maximum likelihood estimation. There are various techniques for estimating parameters with maximum likelihood estimation. We consider two techniques, namely, matrix differentiation and matrix transformation. With both techniques we must derivate likelihood function that we get from the sample. We also consider monotone sample, which is a special case of missing data for which we can also estimate parameters with method of maximum likelihood estimation.