MATLAB doesn't provide a specific function to remove outliers. In general you have a couple different options to deal with outliers.
1. You can create an index that flags potential outliers and either delete them from your data set or substitute more plausible values
2. You can use robust techniques like robust regression which are less sensitive to the presence of outliers.
Your choice of strategies will depend a lot on your knowledge about the data set. For example, if you have a lot of data points that are coded with a value like -9999 these are probably error codes of some kind rather than actual numeric information.
I'm including some simple example code which shows a standard technique to detect outliers.
s = RandStream('mt19937ar','seed',1966);
X = 1:100;
X = X';
noise = randn(100,1);
noise2 = 10*randn(100,1);
noise(11:20:91) = noise2(11:20:91);
Y = 3*X + 2 + noise;
stats = regstats(Y,X,'linear');
potential_outlier = stats.cookd > 4/length(X);