This paper explores the use of classifier ensembles for the task of named entity recognition (NER) on a Dutch dataset. Classifiers from 3 classification frameworks, namely memory-based learning (MBL), conditional random fields (CRF) and support vector machines (SVM), were trained on 8 different feature sets to create a pool of classifiers from which an ensemble could be built. A genetic algorithm approach was used to find the optimal ensemble combination, given various voting mechanisms for combining classifier outputs. The experiments yielded a classifier ensemble that outperformed the best individual classifier by 0.67 percentage points (F-score), a small but statistically significant margin. Experimental results also showed that ensembling classifiers from different frameworks benefits generalization performance.