Fashion sales forecasting is very important for the retail industry and accurate sales forecasting can improve the final fashion sales profits greatly. The current fashion sales forecast data is limited and the data volatility makes it harder to accurately forecast. In order to solve the problems, a new hybrid intelligent prediction algorithm comprising Artificial Neural Network (ANN) and Discrete Grey forecasting Model (DGM(1,1)) was proposed. The Correlation Analysis (CA) was used to get important influence variables with large correlation and DGM(1,1)+ANN were used to forecast the sales data. Then the residual of real sales data and the forecasting results of DGM(1,1)+ANN was added into influence variables for forecasting the second residual by using ANN and adopting an idea of secondary residual. Finally, the experiments based on real data sets of fashion sales were conducted to evaluate the feasibility and accuracy of the proposed hybrid algorithm. The experimental results show that, in forecasting fashion sales data, the forecasting Mean Absolute Percent Error (MAPE) of the proposed algorithm is about 25%. The forecast accuracy has greatly improved, compared to AutoregRessive Integrated Moving Average model (ARIMA), Extended Extreme Learning Machine (EELM), DGM(1,1), DGM(1,1)+ANN algorithm, the average forecasting accuracy is improved about 8 percentage points. The proposed hybrid intelligent algorithm for fashion sales can be used for real-time sales forecasting and improve sales greatly.