Alignment of sequence reads is an important step of many bioinformatics workflows. While the alignment of short reads is well investigated, the alignment of long reads produced by third-generation sequencing technologies, such as Oxford Nanopore, is more challenging because they have high error rates (10-40%). Furthermore, due to their different algorithmic approaches, different tools produce varied alignments, significantly influencing the downstream analyses. In this study, we evaluated the performance of three alignment tools (LAST, GraphMap, and NanoBLASTer) using simulated nanopore reads. Although the three alignment strategies gave similar results (e.g., all close to 100% precision), GraphMap reported the longest alignments while LAST the shortest. However, GraphMap showed the lowest recall (90%) indicating high false negative rates. While GraphMap had the highest percentage of reads that were mapped to the correct reference regions, NanoBLASTer and especially LAST mapped the majority of the reads only partially correctly. Based on our multiple statistics, GraphMap had the best overall performance.