Abstract: The methods presented in this paper include using auto-associative memory which can be defined as a supervised organizing method which is a specific type of h-sorting which is compatible with the morphologic operators over multi-variables data. Mathematical morphologies for multi-variable images require appropriate sort descriptions that allow us to define and use primitive morphologies operators without any wrong results such as wrong color. All the required calculations are defined with lattice algebra (+,^ and ∨); therefore, the proposed method will be faster with less computation overhead than the previous methods. This method does not use any assumptions of the stochastic process which means that this method is independent of the model. The presented method uses cellular learning automata which results in fewer errors than the mathematical methods due to the feedback from the network
