Restriction Fragment Length Polymorphisms (RFLPs) have been explored by plant breeders and molecular biologists for their potential in plant breeding. In this study, RFLPs were used to locate chromosome regions for the following three categories of traits: resistance to second generation European corn borer (Ostrinia nubilalis Hubner) (2ECB), morphological traits (plant height, ear height, anthesis and silk emergence) and yield component traits (grain yield/plot, ear number/plot, 300-kernel weight, number of kernel rows/ear, ear length, ear diameter, cob diameter and kernel depth);A segregating population was created by crossing inbreds B52 and Mo17 and self-pollinating F[subscript]1 hybrid to form the F[subscript]2 population. A random sample of 150 F[subscript]2:3 lines was used for RFLP analysis and replicated field evaluation. One hundred and thirteen maize genomic and cDNA clones were included to construct a RFLP linkage map. Quantitative trait loci (QTL) for the traits were identified based on the linkage map. Two procedures were employed to conduct the analysis of QTL location, interval mapping and single marker analysis in a linear model;Putative QTL for resistance to 2ECB were located to chromosomes 1, 2, 3, 4, 7, 8, 9 and 10. The regions with major effects tended to be detected in more than one environment, and were derived from B52. All chromosome arms detected by a previous translocation study were identified by RFLP analysis;RFLPs were able to detect chromosome regions for every trait in two environments except for kernel depth. In general, regions with high LOD scores were detected in more than one environment, and regions with smaller effects were detected in one environment;Single marker analysis detected more dominance variation for morphological traits and several yield component traits. Dominance might contribute to the total genetic variation more than additive variation. The estimates of high dominance might be caused by the maximized linkage disequilibrium in an F[subscript]2 population;QTL of highly correlated traits were found on the same chromosome arms and often denoted by the same intervals. The result supported the classical quantitative genetics theory regarding the cause of correlation among traits, linkage and/or pleiotropy;Yield component traits were analyzed for the interaction between marker and environments. The proposed method seemed to be able to detect the differences of QTL performance across environments.