Error Correcting Output Coding is a common technique for multiple class classification tasks which decomposes the original problem in several two-class problems solved through dichotomizers. Such classification system can be improved with a reject option which can be defined according to the level of information available from the dichotomizers. This paper analyzes how this knowledge is useful when applying such reject rules. The nature of the outputs, the kind of the employed classifiers and the knowledge of their loss function are influential details for the improvement of the general performance of the system. Experimental results on popular benchmark data sets are reported to show the behavior of the different schemes.