Hereditary spastic paraplegias (HSP) are a heterogeneous group of neurological disorders. Insidiously progressive spastic weakness of the lower extremities is the common criterion in all forms described. Clinically, HSP is differentiated into pure (uncomplicated) and complex (complicated) forms. While pure HSP is predominantly characterized by signs and symptoms of pyramidal tract dysfunction, additional neurological and non-neurological symptoms occur in complicated forms. Autosomal dominant, autosomal recessive, and X-linked modes of inheritance have been described and at least 48 subtypes, termed SPG1-48, have been genetically defined. Although in autosomal dominant HSP families 50-60% of etiologies can be established by genetic testing, genotype predictions based on the phenotype are limited. In order to realize high-throughput genotyping for dominant HSP, we designed a resequencing microarray for six autosomal dominant genes on the Affymetrix CustomSEQ array platform. For validation purposes, 10 previously Sanger sequenced patients with autosomal dominant HSP and 40 positive controls with known mutations in ATL1, SPAST, NIPA1, KIF5A, and BSCL2 (32 base exchanges, eight small indels) were resequenced on this array. DNA samples of 45 additional patients with AD spastic paraplegia were included in the study. With two different sequencing analysis software modules (GSEQ, SeqC), all missense/nonsense mutations in the positive controls were identified while indels had a detection rate of only 50%. In total, 244 common synonymous single-nucleotide polymorphisms (SNPs) annotated in dbSNP (build 132) corresponding to 22 distinct sequence variations were found in the 53 analyzed patients. Among the 22 different sequence variations (SPAST n = 15, ATL1 n = 3, KIF5A n = 2, HSPD1 n = 1, BSCL2 n = 1, NIPA1 n = 0), 12 were rare variants that have not been previously described and whose clinical significance is unknown. In SPAST-negative cases, a genetic diagnosis could be established in 11% by resequencing. Resequencing microarray technology can therefore efficiently be used to study genotypes and mutations in large patient cohorts.