The decline of fertility in the UK dairy herd and the unfavorable genetic correlation (r(a)) between fertility and milk yield has necessitated the broadening of breeding goals to include fertility. The coefficient of genetic variation present in fertility is of similar magnitude to that present in production traits; however, traditional measurements of fertility (such as calving interval, days open, nonreturn rate) have low heritability (h2 < 0.05), and recording is often poor, hindering identification of genetically superior animals. An alternative approach is to use endocrine measurements of fertility such as interval to commencement of luteal activity postpartum (CLA), which has a higher h2 (0.16 to 0.23) and is free from management bias. Although CLA has favorable phenotypic correlations with traditional measures of fertility, if it is to be used in a selection index, the genetic correlation (ra) of this trait with fertility and other components of the index must be estimated. The aim of the analyses reported here was to obtain information on the ra between lnCLA and calving interval (CI), average body condition score (BCS; one to nine, an indicator of energy balance estimated from records taken at different months of lactation), production and a number of linear type traits. Genetic models were fitted using ASREML, and r(a) were inferred from genetic regression of lnCLA on sire-predicted transmitting abilities (PTA) for the trait concerned by multiplying the regression coefficient (b) by the ratio of the genetic standard deviations. The inferred r(a) between lnCLA and CI and average BCS were 0.36 and -0.84, respectively. Genetic correlations between InCLA and milk fat and protein yields were all positive and ranged between 0.33 and 0.69. Genetic correlations between InCLA and linear type traits reflecting body structure ranged from -0.25 to 0.15, and between udder characteristics they ranged from -0.16 to 0.05. Thus, incorporation of endocrine parameters of fertility, such as CIA, into a fertility index may offer the potential to improve the accuracy of breeding value prediction for fertility, thus allowing producers to make more informed selection decisions.