Genomic selection (GS) can increase the genetic gain in plants. In perennial crops, this can be achieved via shortened breeding cycles and increased selection intensity. Our objective was to obtain the first empirical estimate of GS accuracy in oil palm (Elaeis guineensis), where the main challenge is to obtain sufficient accuracy to train GS models, despite small populations. We used two parental populations involved in conventional reciprocal recurrent selection (Deli and Group B) with 131 individuals each, genotyped with 265 SSR. We estimated the within population GS accuracy when predicting masked estimated breeding values for eight yield traits. We used three methods to sample training sets and five statistical methods to estimate genomic breeding values. The results showed that in Group B, GS could achieve higher accuracy than the pedigree-based model, indicating that GS could account for family effects and Mendelian sampling terms. The GS accuracy ranged from -0.41 to 0.94 and was correlated with the relationship between training and test sets (amax). Training sets optimized with CDmean gave the highest amax and accuracies, ranging from 0.49 to 0.94. The statistical methods did not affect the GS accuracy. Finally, Group B individuals could be preselected for progeny tests by applying GS to key yield traits.