It means your design matrix is not invertible and therefore can't be used to develop a regression model. This results from linearly dependent columns, i.e. strongly correlated variables. Examine the pairwise covariance (or correlation) of your variables to investigate if there are any variables that can potentially be removed. You're looking for covariances (or correlations) >> 0. Alternatively, you can probably automate this variable selection by using a forward stepwise regression.
This can also result from having more variables than observations, in which case your design matrix is probably not full rank. This is a bit trickier to fix, but there are ways. I believe lasso regression is supposed to work well when the data is "wider" than it is "long."
Keep in mind: if you decide to try lasso or stepwise selection, your doing much more (in terms of variable selection) than just handling multicolinearity.