Association methods based on haplotype similarity (HS) can overcome power and stability issues encountered in standard haplotype analyses. Current HS methods can be generally classified into evolutionary and two-sample approaches. We propose a new regression-based HS association method for case-control studies that incorporates covariate information and combines the advantages of the two classes of approaches by using inferred ancestral haplotypes. We first estimate the ancestral haplotypes of case individuals and then, for each individual, an ancestral-haplotype-based similarity score is computed by comparing that individual's observed genotype with the estimated ancestral haplotypes. Trait values are then regressed on the similarity scores. Covariates can easily be incorporated into this regression framework. To account for the bias in the raw p-values due to the use of case data in constructing ancestral haplotypes, as well as to account for variation in ancestral haplotype estimation, a permutation procedure is adopted to obtain empirical p-values. Compared with the standard haplotype score test and the multilocus T(2) test, our method improves power when neither the allele frequency nor linkage disequilibrium between the disease locus and its neighboring SNPs is too low and is comparable in other scenarios. We applied our method to the Genetic Analysis Workshop 15 simulated SNP data and successfully pinpointed a stretch of SNPs that covers the fine-scale region where the causal locus is located.