diff --git a/Project.toml b/Project.toml
index 1c5bc128..a9af31da 100644
--- a/Project.toml
+++ b/Project.toml
@@ -11,6 +11,7 @@ CuArrays = "3a865a2d-5b23-5a0f-bc46-62713ec82fae"
 DelimitedFiles = "8bb1440f-4735-579b-a4ab-409b98df4dab"
 Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196"
 Juno = "e5e0dc1b-0480-54bc-9374-aad01c23163d"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
 NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
diff --git a/docs/src/training/optimisers.md b/docs/src/training/optimisers.md
index 1334f07d..0cf165b6 100644
--- a/docs/src/training/optimisers.md
+++ b/docs/src/training/optimisers.md
@@ -140,3 +140,16 @@ ExpDecay
 InvDecay
 WeightDecay
 ```
+
+## Gradient Clipping
+
+Gradient clipping is useful for training recurrent neural networks, which have a tendency to suffer from the exploding gradient problem. An example usage is
+
+```julia
+opt = Optimiser(ClipValue(1e-3), ADAM(1e-3))
+```
+
+```@docs
+ClipValue
+ClipNorm
+```
\ No newline at end of file
diff --git a/src/Flux.jl b/src/Flux.jl
index 90dcb630..c28a7d36 100644
--- a/src/Flux.jl
+++ b/src/Flux.jl
@@ -3,7 +3,8 @@ module Flux
 # Zero Flux Given
 
 using Base: tail
-using Zygote, MacroTools, Juno, Reexport, Statistics, Random
+using Statistics, Random, LinearAlgebra
+using Zygote, MacroTools, Juno, Reexport
 using MacroTools: @forward
 @reexport using NNlib
 using Zygote: Params, @adjoint, gradient, pullback, @nograd
@@ -20,7 +21,8 @@ using .Optimise
 using .Optimise: @epochs
 export Descent, ADAM, Momentum, Nesterov, RMSProp,
   ADAGrad, AdaMax, ADADelta, AMSGrad, NADAM,
-  ADAMW, RADAM, InvDecay, ExpDecay, WeightDecay
+  ADAMW, RADAM, InvDecay, ExpDecay, WeightDecay,
+  ClipValue, ClipNorm
 
 
 using CuArrays
diff --git a/src/optimise/Optimise.jl b/src/optimise/Optimise.jl
index 5ffe4a8f..0f5e644f 100644
--- a/src/optimise/Optimise.jl
+++ b/src/optimise/Optimise.jl
@@ -1,9 +1,12 @@
 module Optimise
 
+using LinearAlgebra
+
 export train!, update!,
 	Descent, ADAM, Momentum, Nesterov, RMSProp,
 	ADAGrad, AdaMax, ADADelta, AMSGrad, NADAM, ADAMW,RADAM, 
-	InvDecay, ExpDecay, WeightDecay, stop, Optimiser
+	InvDecay, ExpDecay, WeightDecay, stop, Optimiser,
+	ClipValue, ClipNorm
 
 include("optimisers.jl")
 include("train.jl")
diff --git a/src/optimise/optimisers.jl b/src/optimise/optimisers.jl
index 611edddb..466b7b6d 100644
--- a/src/optimise/optimisers.jl
+++ b/src/optimise/optimisers.jl
@@ -533,3 +533,31 @@ function apply!(o::WeightDecay, x, Δ)
   wd = o.wd
   @. Δ += wd * x
 end
+
+"""
+    ClipValue(thresh)
+
+Clip gradients when their absolute value exceeds `thresh`.
+"""
+mutable struct ClipValue{T}
+    thresh::T
+end
+
+apply!(o::ClipValue, x, Δ) = clamp!(Δ, -o.thresh, o.thresh)
+
+"""
+    ClipNorm(thresh)
+
+Clip gradients when their L2 norm exceeds `thresh`.
+"""
+mutable struct ClipNorm{T}
+    thresh::T
+end
+
+function apply!(o::ClipNorm, x, Δ)
+    Δnrm = norm(Δ)
+    if Δnrm > o.thresh
+        rmul!(Δ, o.thresh / Δnrm)
+    end
+    return Δ
+end
\ No newline at end of file
diff --git a/test/optimise.jl b/test/optimise.jl
index ac131b96..b3a0250c 100644
--- a/test/optimise.jl
+++ b/test/optimise.jl
@@ -89,3 +89,15 @@ end
     @test decay_steps == ground_truth
     @test o.eta == o.clip
 end
+
+@testset "Clipping" begin
+    w = randn(10, 10)
+    loss(x) = sum(w * x)
+    θ = Params([w])
+    x = 1000 * randn(10)
+    w̄ = gradient(() -> loss(x), θ)[w]
+    w̄_value = Optimise.apply!(ClipValue(1.0), w, copy(w̄))
+    @test all(w̄_value .<= 1)
+    w̄_norm = Optimise.apply!(ClipNorm(1.0), w, copy(w̄))
+    @test norm(w̄_norm) <= 1
+end
\ No newline at end of file