Observation of Gait patterns is the available evaluation in clinical routine of the motor manifestations in Parkinson's Disease (PD). Lately, different investigations have attempted to quantitatively analyze gait patterns by linear methods facing several limitations since the non-stationary nature of the gait patterns. This study presents a non-linear characterization of the Parkinson's disease gait by a deterministic chaotic analysis which represents the temporal gait dynamics with a minimum set of parameters. Specifically, delay and embedding dimension parameters were obtained for reconstructing the phase space and its characteristic coefficients, namely Lyapunov, correlation dimension, and approximate entropy. Statistical differences (p < 0.05, Mann-Whitney test) were found for the Lyapunov exponent and the approximated entropy when describing two gait patterns, i.e., control and PD groups.