Since sub-sentential alignment is critically important to the translation quality of an Example-Based Machine Translation (EBMT) system, which operates by finding and combining phrase-level matches against the training examples, we developed a new alignment algorithm for the purpose of improving the EBMT system’s performance. This new Symmetric Probabilistic Alignment (SPA) algorithm treats the source and target languages in a symmetric fashion. We describe our basic algorithm and its primary extensions that enable use of surrounding context, and of positional preference information, compare its alignment accuracy with IBM Model 4, and report on experiments in which either IBM Model 4 or SPA alignments are substituted for the aligner currently built into the EBMT system. Both Model 4 and SPA are significantly better than the internal aligner. Then we extend SPA to exploit external alignment information from Moses and to output non-contiguous target phrases. We also alter SPA so that the weights for its feature scores are tuned using minimum error rate training. Our experiments show that exploiting