Niemann-Pick disease type C (NPC), a neurovisceral disorder characterized by accumulation of unesterified cholesterol and glycolipids in the lysosomal/late endosomal system, is due to mutations on either the NPC1 or the NPC2 genes. While the corresponding proteins appear essential for proper cellular cholesterol trafficking, their precise function and relationship are still unclear. Mutational analysis of patients, useful for the study of structure/function relationships, is especially valuable for proper management of affected families. Correlations have been found between genotypes and the severity of the neurological outcome of the patients, and molecular genetics constitutes the optimal approach for prenatal diagnosis. However, mutation detection in NPC disease is a challenge. The NPC1 gene, affected in >95% of the families, is large in size (approximately 50 kb), and the already known disease-causing mutations and numerous polymorphisms are scattered over 25 exons. Furthermore, detection of NPC2 patients by complex genetic complementation tests is unpractical. In the present study, we describe a rapid and reliable strategy for detecting NPC genetic variations using DHPLC analysis. Conditions of analysis were optimized for all the NPC1 and NPC2 30 exons and validated using 38 previously genotyped patients. These conditions were then applied to screen a panel of 35 genetically uncharacterized, unrelated NPC patients. Pathogenic mutations were identified in 68/70 alleles. Among the mutations identified, 29 were novel, including two of the NPC2 gene. We conclude that DHPLC is a rapid, low-cost, highly accurate, and efficient technique for the detection of NPC genetic variants.