Feature selection is an important pre-processing step in classification tasks. Feature selection aims to minimise both the classification error rate and the number of features, which are usually two conflicting objectives. This paper develops a differential evolution (DE) based multi-objective feature selection ap- proach. The multi-objective approach is compared with two conventional meth- ods and two DE based single objective methods, where the first algorithm is to minimise the classification error rate only while the second algorithm combines the number of features and the classification error rate into a single fitness func-tion. Their performances are examined on nine different datasets and the results show that the proposed multi-objective al gorithm successfully evolved a number of trade-off solutions, which reduce the number of features and keep or reduce the classification error rate. In almost all cases, the proposed multi-objective al- gorithm achieved better performance than all the other four methods in terms of both the classification accuracy and the number of features.