This paper presents a comparative study on the applications of general regression neural network (GRNN) models and conventional Box–Jenkins time series models to predict the maintenance cost of construction equipment. The comparison is based on the generic time series analysis assumption that time-sequenced observations have serial correlations within the time series and cross correlations with the explanatory time series. Both GRNN and Box–Jenkins time series models can describe the behavior and predict the maintenance costs of different equipment categories and fleets with an acceptable level of accuracy. Forecasting with multivariate GRNN models was improved significantly after incorporating parallel fuel consumption data as an explanatory time series. An accurate forecasting of equipment maintenance cost into the future can facilitate decision support tasks such as equipment budget and resource planning, equipment replacement, and determining the internal rate of charge on equipment use.