The objective of this study was to apply principal component analysis (PCA) and multiple correspondence analysis (MCA) on Dairy Herd Improvement (DHI) data of animals on their first lactation to discover the most meaningful set of variables that describe the outcome on the first test day. Data collected over 4 years were obtained from 13 dairy herds located in Québec – Canada. The data set was filtered to contain only information from first test day of animals on their first lactation, resulting in 1637 observations and 35 variables. Eight additional variables were created from the existing DHI metrics. PCA was performed on numeric variables (n = 14) after they were standardized to mean = 0 and standard deviation = 1. MCA was performed on categorical variables (n = 20). Seven numerical variables and eight categorical variables were selected as meaningful to describe the variation on the first test day using PCA and MCA. These variables could be used to evaluate the outcome on the first test day of animals on their first lactation and assist in the evaluation of their transition period. Future work could focus on modeling the relationship between those variables.