This paper presents an Instance Space Analysis (ISA) methodology to address the problem of understanding the complex relationship between instance features and metaheuristic (MH) performance in the vehicle routing problem (CVRP). Using the dataset of DIMACS 12th Implementation Challenge, we identify 23 relevant instance features and analyze the impact of instance structure on the behavior of MH by projecting the instance space into two dimensions through PRELIM, SIFTED, and PILOT steps utilizing dimensionality reduction and machine learning techniques. The key contribution is to provide a new instance analysis method in the CVRP field by providing a projection matrix that can easily integrate new instances into the analysis.