diff --git a/ext/TensorKitChainRulesCoreExt/factorizations.jl b/ext/TensorKitChainRulesCoreExt/factorizations.jl
index 676204ba3..ba1a82dde 100644
--- a/ext/TensorKitChainRulesCoreExt/factorizations.jl
+++ b/ext/TensorKitChainRulesCoreExt/factorizations.jl
@@ -219,7 +219,7 @@ function svd_pullback!(ΔA::AbstractMatrix, U::AbstractMatrix, S::AbstractVector
     Sp = view(S, 1:p)
 
     # rank
-    r = findlast(>=(tol), S)
+    r = count(>(tol), S)
 
     # compute antihermitian part of projection of ΔU and ΔV onto U and V
     # also already subtract this projection from ΔU and ΔV
diff --git a/src/TensorKit.jl b/src/TensorKit.jl
index ccbdd94ff..045f83c05 100644
--- a/src/TensorKit.jl
+++ b/src/TensorKit.jl
@@ -72,7 +72,7 @@ export mul!, lmul!, rmul!, adjoint!, pinv, axpy!, axpby!
 export leftorth, rightorth, leftnull, rightnull,
        leftorth!, rightorth!, leftnull!, rightnull!,
        tsvd!, tsvd, eigen, eigen!, eig, eig!, eigh, eigh!, exp, exp!,
-       isposdef, isposdef!, ishermitian, sylvester
+       isposdef, isposdef!, ishermitian, sylvester, rank, cond
 export braid, braid!, permute, permute!, transpose, transpose!, twist, twist!, repartition,
        repartition!
 export catdomain, catcodomain
@@ -119,7 +119,7 @@ using LinearAlgebra: norm, dot, normalize, normalize!, tr,
                      adjoint, adjoint!, transpose, transpose!,
                      lu, pinv, sylvester,
                      eigen, eigen!, svd, svd!,
-                     isposdef, isposdef!, ishermitian,
+                     isposdef, isposdef!, ishermitian, rank, cond,
                      Diagonal, Hermitian
 
 using SparseArrays: SparseMatrixCSC, sparse, nzrange, rowvals, nonzeros
diff --git a/src/tensors/linalg.jl b/src/tensors/linalg.jl
index a3f84adb1..395c49f27 100644
--- a/src/tensors/linalg.jl
+++ b/src/tensors/linalg.jl
@@ -271,6 +271,32 @@ function _norm(blockiter, p::Real, init::Real)
     end
 end
 
+_default_rtol(t) = eps(real(float(scalartype(t)))) * min(dim(domain(t)), dim(codomain(t)))
+
+function LinearAlgebra.rank(t::AbstractTensorMap; atol::Real=0,
+                            rtol::Real=atol > 0 ? 0 : _default_rtol(t))
+    dim(t) == 0 && return 0
+    S = LinearAlgebra.svdvals(t)
+    tol = max(atol, rtol * maximum(first, values(S)))
+    return sum(cs -> dim(cs[1]) * count(>(tol), cs[2]), S)
+end
+
+function LinearAlgebra.cond(t::AbstractTensorMap, p::Real=2)
+    if p == 2
+        if dim(t) == 0
+            domain(t) == codomain(t) ||
+                throw(SpaceMismatch("`cond` requires domain and codomain to be the same"))
+            return zero(real(float(scalartype(t))))
+        end
+        S = LinearAlgebra.svdvals(t)
+        maxS = maximum(first, values(S))
+        minS = minimum(last, values(S))
+        return iszero(maxS) ? oftype(maxS, Inf) : (maxS / minS)
+    else
+        throw(ArgumentError("cond currently only defined for p=2"))
+    end
+end
+
 # TensorMap trace
 function LinearAlgebra.tr(t::AbstractTensorMap)
     domain(t) == codomain(t) ||
diff --git a/test/tensors.jl b/test/tensors.jl
index 0441dd313..0dcf21300 100644
--- a/test/tensors.jl
+++ b/test/tensors.jl
@@ -546,6 +546,23 @@ for V in spacelist
                             @test b ≈ s′[c]
                         end
                     end
+                    @testset "cond and rank" begin
+                        t2 = permute(t, ((3, 4, 2), (1, 5)))
+                        d1 = dim(codomain(t2))
+                        d2 = dim(domain(t2))
+                        @test rank(t2) == min(d1, d2)
+                        M = leftnull(t2)
+                        @test rank(M) == max(d1, d2) - min(d1, d2)
+                        t3 = unitary(T, V1 ⊗ V2, V1 ⊗ V2)
+                        @test cond(t3) ≈ one(real(T))
+                        @test rank(t3) == dim(V1 ⊗ V2)
+                        t4 = randn(T, V1 ⊗ V2, V1 ⊗ V2)
+                        t4 = (t4 + t4') / 2
+                        vals = LinearAlgebra.eigvals(t4)
+                        λmax = maximum(s -> maximum(abs, s), values(vals))
+                        λmin = minimum(s -> minimum(abs, s), values(vals))
+                        @test cond(t4) ≈ λmax / λmin
+                    end
                 end
                 @testset "empty tensor" begin
                     t = randn(T, V1 ⊗ V2, zero(V1))
@@ -586,6 +603,13 @@ for V in spacelist
                         @test U == t
                         @test dim(U) == dim(S) == dim(V)
                     end
+                    @testset "cond and rank" begin
+                        @test rank(t) == 0
+                        W2 = zero(V1) * zero(V2)
+                        t2 = rand(W2, W2)
+                        @test rank(t2) == 0
+                        @test cond(t2) == 0.0
+                    end
                 end
                 t = rand(T, V1 ⊗ V1' ⊗ V2 ⊗ V2')
                 @testset "eig and isposdef" begin
@@ -615,6 +639,7 @@ for V in spacelist
                     @test V ≈ Ṽ
                     λ = minimum(minimum(real(LinearAlgebra.diag(b)))
                                 for (c, b) in blocks(D))
+                    @test cond(Ṽ) ≈ one(real(T))
                     @test isposdef(t2) == isposdef(λ)
                     @test isposdef(t2 - λ * one(t2) + 0.1 * one(t2))
                     @test !isposdef(t2 - λ * one(t2) - 0.1 * one(t2))