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Vertical inline pump is a singlestage single suction centrifugal pump with a bent pipe before the impeller, which is usually used in where the constraint is installation space such as pumphouses. But these unavoidable bents before the impeller inlet also result in the hydraulic losses at the entry of the pump and the decrease of efficiency. In order to improve the performance of a vertical inline pump, an optimization on inlet pipe was proposed based on artificial neural network (ANN) and particle swarm optimization (PSO). The profile of inlet pipe was controlled by the mid curve and the shape of cross sections. The shape of mid curve was fitted by using a fifth ordered Bezier curve and the trend of parameters of cross sections along the mid curve were fitted by third ordered Bezier curves. Considering the real installation of the pump, totally 11 design parameters of inlet pipe were set as the design variables and the efficiency of the pump was set as the objective function. In order to build highprecision ANN model between the objective function and the 11 design variables, totally 149 groups of sample data were created by using Latin hypercube sampling. After that, the ANN model was solved for the optimum solution of the design variables of inlet pipe by using particle swarm optimization. The result showed that there was a good agreement between computational results and experimental results; the ANN model could accurately fit the objective function and variables, the deviation between predicted value and actual value was 0.32%; after optimization, the efficiency and head of the pump was increased by 1.17 percentage points and 0.23m, respectively. The highefficiency period was also expended. Compared with the original inlet pipe, the flow condition in inlet pipe was improved after optimization.